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1.  A Study of New Pulse Auscultation System 
Sensors (Basel, Switzerland)  2015;15(4):8712-8731.
This study presents a new type of pulse auscultation system, which uses a condenser microphone to measure pulse sound waves on the wrist, captures the microphone signal for filtering, amplifies the useful signal and outputs it to an oscilloscope in analog form for waveform display and storage and delivers it to a computer to perform a Fast Fourier Transform (FFT) and convert the pulse sound waveform into a heartbeat frequency. Furthermore, it also uses an audio signal amplifier to deliver the pulse sound by speaker. The study observed the principles of Traditional Chinese Medicine’s pulsing techniques, where pulse signals at places called “cun”, “guan” and “chi” of the left hand were measured during lifting (100 g), searching (125 g) and pressing (150 g) actions. Because the system collects the vibration sound caused by the pulse, the sensor itself is not affected by the applied pressure, unlike current pulse piezoelectric sensing instruments, therefore, under any kind of pulsing pressure, it displays pulse changes and waveforms with the same accuracy. We provide an acquired pulse and waveform signal suitable for Chinese Medicine practitioners’ objective pulse diagnosis, thus providing a scientific basis for this Traditional Chinese Medicine practice. This study also presents a novel circuit design using an active filtering method. An operational amplifier with its differential features eliminates the interference from external signals, including the instant high-frequency noise. In addition, the system has the advantages of simple circuitry, cheap cost and high precision.
PMCID: PMC4431284  PMID: 25875192
pulse; auscultation; condenser microphone
2.  MED40/472: A WWW Multimedia Textbook of Internal Propedeutics 
Traditional ways of teaching techniques of physical examinations in the first clinical courses are rather demanding in terms of teacher involvement and a pool of patients suitable for demonstrations. For a long time, various audio-visual tools have been used to save teachers' and students' time and patients' patience. The modern technology of WWW publishing of multimedia allows good access to such teaching materials - and there already exist several collections of heart sounds, breath sounds etc. The aim of our project is to design and set up a comprehensive multimedia textbook of internal propedeutics that would present various physiological and pathological findings (auscultation, inspection, basic imaging) in the context of diagnostic patient investigation - the status praesens - as it is taught in the first clinical courses.
Unlike classical textbooks, hypertext presentation allows to ogranize the material into several structures - reflecting various approaches: systemic (digestive, cardiovascular etc.) approach, nosological, differential diagnoses, etc. To identify and implement the various useful approaches is the most difficult part of the task. The accompanying illustrative material is being prepared with the use of modern technologies - digital camera, scanner, video-camera and digitizer, digital audio recording, etc.
In the first year of the project, the skeleton of the multimedia presentation is being constructed - corresponding to the various approaches to the subject. Concurrently, suitable illustrative material is being gathered from cases of the Internal Clinic. Various existing WWW presentations dealing with heart and breath sounds and other relevant investigations have been searched and listed.
Experience and feedback from other projects of this type confirm that a rather elaborate logical and technical construction of multimedia textbooks is rewarded by a good acceptance by both students and teachers. Good access to Internet, sufficient for multimedia transfers, however, is a necessary prerequisite. Internal propedeutics is a very suitable field for internet-based multimedia textbooks: instant access to audio and video recordings is much welcome in development of clinical skills. The project is supported by a grant of the Czech Universities Development Fund.
PMCID: PMC1761808
Medical Education; Distance Education; Internet; Multimedia; Internal Medicine; Physical Examination
3.  Characterizing Nonlinear Heartbeat Dynamics within a Point Process Framework 
Human heartbeat intervals are known to have nonlinear and nonstationary dynamics. In this paper, we propose a model of R–R interval dynamics based on a nonlinear Volterra–Wiener expansion within a point process framework. Inclusion of second-order nonlinearities into the heartbeat model allows us to estimate instantaneous heart rate (HR) and heart rate variability (HRV) indexes, as well as the dynamic bispectrum characterizing higher order statistics of the nonstationary non-Gaussian time series. The proposed point process probability heartbeat interval model was tested with synthetic simulations and two experimental heartbeat interval datasets. Results show that our model is useful in characterizing and tracking the inherent nonlinearity of heartbeat dynamics. As a feature, the fine temporal resolution allows us to compute instantaneous nonlinearity indexes, thus sidestepping the uneven spacing problem. In comparison to other nonlinear modeling approaches, the point process probability model is useful in revealing nonlinear heartbeat dynamics at a fine timescale and with only short duration recordings.
PMCID: PMC2952361  PMID: 20172783
Adaptive filters; approximate entropy (ApEn); heart rate variability (HRV); nonlinearity test; point processes; scaling exponent; Volterra series expansion
4.  Detection of inter-patient left and right bundle branch block heartbeats in ECG using ensemble classifiers 
Left bundle branch block (LBBB) and right bundle branch block (RBBB) not only mask electrocardiogram (ECG) changes that reflect diseases but also indicate important underlying pathology. The timely detection of LBBB and RBBB is critical in the treatment of cardiac diseases. Inter-patient heartbeat classification is based on independent training and testing sets to construct and evaluate a heartbeat classification system. Therefore, a heartbeat classification system with a high performance evaluation possesses a strong predictive capability for unknown data. The aim of this study was to propose a method for inter-patient classification of heartbeats to accurately detect LBBB and RBBB from the normal beat (NORM).
This study proposed a heartbeat classification method through a combination of three different types of classifiers: a minimum distance classifier constructed between NORM and LBBB; a weighted linear discriminant classifier between NORM and RBBB based on Bayesian decision making using posterior probabilities; and a linear support vector machine (SVM) between LBBB and RBBB. Each classifier was used with matching features to obtain better classification performance. The final types of the test heartbeats were determined using a majority voting strategy through the combination of class labels from the three classifiers. The optimal parameters for the classifiers were selected using cross-validation on the training set. The effects of different lead configurations on the classification results were assessed, and the performance of these three classifiers was compared for the detection of each pair of heartbeat types.
The study results showed that a two-lead configuration exhibited better classification results compared with a single-lead configuration. The construction of a classifier with good performance between each pair of heartbeat types significantly improved the heartbeat classification performance. The results showed a sensitivity of 91.4% and a positive predictive value of 37.3% for LBBB and a sensitivity of 92.8% and a positive predictive value of 88.8% for RBBB.
A multi-classifier ensemble method was proposed based on inter-patient data and demonstrated a satisfactory classification performance. This approach has the potential for application in clinical practice to distinguish LBBB and RBBB from NORM of unknown patients.
PMCID: PMC4086987  PMID: 24903422
Heartbeat classification; Left bundle branch block (LBBB); Right bundle branch block (RBBB); Independent component analysis (ICA); Linear discriminant classifier; Support vector machine (SVM); Ensemble
5.  Acoustic cardiac signals analysis: a Kalman filter–based approach 
Auscultation of the heart is accompanied by both electrical activity and sound. Heart auscultation provides clues to diagnose many cardiac abnormalities. Unfortunately, detection of relevant symptoms and diagnosis based on heart sound through a stethoscope is difficult. The reason GPs find this difficult is that the heart sounds are of short duration and separated from one another by less than 30 ms. In addition, the cost of false positives constitutes wasted time and emotional anxiety for both patient and GP. Many heart diseases cause changes in heart sound, waveform, and additional murmurs before other signs and symptoms appear. Heart-sound auscultation is the primary test conducted by GPs. These sounds are generated primarily by turbulent flow of blood in the heart. Analysis of heart sounds requires a quiet environment with minimum ambient noise. In order to address such issues, the technique of denoising and estimating the biomedical heart signal is proposed in this investigation. Normally, the performance of the filter naturally depends on prior information related to the statistical properties of the signal and the background noise. This paper proposes Kalman filtering for denoising statistical heart sound. The cycles of heart sounds are certain to follow first-order Gauss–Markov process. These cycles are observed with additional noise for the given measurement. The model is formulated into state-space form to enable use of a Kalman filter to estimate the clean cycles of heart sounds. The estimates obtained by Kalman filtering are optimal in mean squared sense.
PMCID: PMC3383292  PMID: 22745550
heart sound; murmurs; ECG; Kalman filters; acoustic cardiac signals
6.  A Technique for Estimating the Occlusion Effect for Frequencies Below 125 Hz 
Ear and hearing  2014;35(1):49-55.
The level of bone-conducted sound in the auditory meatus is increased at low frequencies by occlusion of the meatus, for example by the earmold of a hearing aid. Physical measures of this “occlusion effect” (OE) require vibration of the skull. In previous research, either self-voicing or audiometric bone-conduction vibrators have been used to produce this vibration, with the result that the OE could not be measured for frequencies below 125 Hz. However, frequencies below this can be important for music perception by hearing aid users. The objective was to develop and evaluate a method that gives a lower-bound estimate of the OE for frequencies below 125 Hz.
A low-noise amplifier with extended low-frequency response was used to record the output of a miniature microphone inserted into the meatus of participants. The signal came from sounds of the heartbeat and blood flow of the participant, transmitted via bone-conduction through the walls of the meatus. A simultaneous recording was made of the carotid pulse to permit time-locked averaging (and hence noise reduction) of the microphone signal. Recordings were made from seven otologically and audiometrically normal participants, using clinical probe tips to produce the occlusion. Recordings were also made from an overlapping group of nine participants, using fast-setting impression material to provide a more consistent degree of occlusion. The difference in level of the recorded signal for unoccluded and occluded conditions provided a lower bound for the magnitude of the OE.
The mean OE increased with decreasing frequency, reaching a plateau of about 40 dB for frequencies below 40 Hz. For some individual recordings, the OE reached 50 dB for frequencies below 20 Hz. With occlusion, the heartbeat became audible for most participants.
The OE can be very large at low frequencies. The use of hearing aids with closed fittings, which may be employed either to prevent acoustic feedback or to allow amplification of low frequencies, may lead to an unacceptable OE. We suggest reducing the OE by the use of a seal deep in the meatus, where the wall of the meatus is more rigid.
PMCID: PMC4197401  PMID: 24141593
Occlusion; earmold; hearing aid; bone conduction; heartbeat
7.  Noninvasive technique for measurement of heartbeat regularity in zebrafish (Danio rerio) embryos 
BMC Biotechnology  2009;9:11.
Zebrafish (Danio rerio), due to its optical accessibility and similarity to human, has emerged as model organism for cardiac research. Although various methods have been developed to assess cardiac functions in zebrafish embryos, there lacks a method to assess heartbeat regularity in blood vessels. Heartbeat regularity is an important parameter for cardiac function and is associated with cardiotoxicity in human being. Using stereomicroscope and digital video camera, we have developed a simple, noninvasive method to measure the heart rate and heartbeat regularity in peripheral blood vessels. Anesthetized embryos were mounted laterally in agarose on a slide and the caudal blood circulation of zebrafish embryo was video-recorded under stereomicroscope and the data was analyzed by custom-made software. The heart rate was determined by digital motion analysis and power spectral analysis through extraction of frequency characteristics of the cardiac rhythm. The heartbeat regularity, defined as the rhythmicity index, was determined by short-time Fourier Transform analysis.
The heart rate measured by this noninvasive method in zebrafish embryos at 52 hour post-fertilization was similar to that determined by direct visual counting of ventricle beating (p > 0.05). In addition, the method was validated by a known cardiotoxic drug, terfenadine, which affects heartbeat regularity in humans and induces bradycardia and atrioventricular blockage in zebrafish. A significant decrease in heart rate was found by our method in treated embryos (p < 0.01). Moreover, there was a significant increase of the rhythmicity index (p < 0.01), which was supported by an increase in beat-to-beat interval variability (p < 0.01) of treated embryos as shown by Poincare plot.
The data support and validate this rapid, simple, noninvasive method, which includes video image analysis and frequency analysis. This method is capable of measuring the heart rate and heartbeat regularity simultaneously via the analysis of caudal blood flow in zebrafish embryos. With the advantages of rapid sample preparation procedures, automatic image analysis and data analysis, this method can potentially be applied to cardiotoxicity screening assay.
PMCID: PMC2664803  PMID: 19228382
8.  Range-dependent flexibility in the acoustic field of view of echolocating porpoises (Phocoena phocoena) 
eLife  null;4:e05651.
Toothed whales use sonar to detect, locate, and track prey. They adjust emitted sound intensity, auditory sensitivity and click rate to target range, and terminate prey pursuits with high-repetition-rate, low-intensity buzzes. However, their narrow acoustic field of view (FOV) is considered stable throughout target approach, which could facilitate prey escape at close-range. Here, we show that, like some bats, harbour porpoises can broaden their biosonar beam during the terminal phase of attack but, unlike bats, maintain the ability to change beamwidth within this phase. Based on video, MRI, and acoustic-tag recordings, we propose this flexibility is modulated by the melon and implemented to accommodate dynamic spatial relationships with prey and acoustic complexity of surroundings. Despite independent evolution and different means of sound generation and transmission, whales and bats adaptively change their FOV, suggesting that beamwidth flexibility has been an important driver in the evolution of echolocation for prey tracking.
eLife digest
Bats and toothed whales such as porpoises have independently evolved the same solution for hunting prey when it is hard to see. Bats hunt in the dark with little light to allow them to see the insects they chase. Porpoises hunt in murky water where different ocean environments can quickly obscure fish from view. So, both bats and porpoises evolved to emit a beam of sound and then track their prey based on the echoes of that sound bouncing off the prey and other objects. This process is called echolocation.
A narrow beam of sound can help a porpoise or bat track distant prey. But as either animal closes in on its prey such a narrow sound beam can be a disadvantage because prey can easily escape to one side. Scientists recently found that bats can widen their sound beam as they close in on prey by changing the frequency—or pitch—of the signal they emit or by adjusting how they open their mouth.
Porpoises, by contrast, create their echolocation clicks by forcing air through a structure in their blowhole called the phonic lips. The sound is transmitted through a fatty structure on the front of their head known as the melon, which gives these animals their characteristic round-headed look, before being transmitted into the sea. Porpoises would also likely benefit from widening their echolocation beam as they approach prey, but it was not clear if and how they could do this.
Wisniewska et al. used 48 tightly spaced underwater microphones to record the clicks emitted by three captive porpoises as they approached a target or a fish. This revealed that in the last stage of their approach, the porpoises could triple the area their sound beam covered, giving them a ‘wide angle view’ as they closed in. This widening of the sound beam occurred during a very rapid series of echolocation signals called a buzz, which porpoises and bats perform at the end of a pursuit. Unlike bats, porpoises are able to continue to change the width of their sound beam throughout the buzz.
Wisniewska et al. also present a video that shows that the shape of the porpoise's melon changes rapidly during a buzz, which may explain the widening beam. Furthermore, images obtained using a technique called magnetic resonance imaging (MRI) revealed that a porpoise has a network of facial muscles that are capable of producing these beam-widening melon distortions.
As both bats and porpoises have evolved the capability to adjust the width of their sound beam, this ability is likely to be crucial for hunting effectively using echolocation.
PMCID: PMC4413254  PMID: 25793440
phocoena phocoena; biosonar; beam directionality; buzz; prey capture; convergent evolution; other
9.  Computerised lung sound analysis to improve the specificity of paediatric pneumonia diagnosis in resource-poor settings: protocol and methods for an observational study 
BMJ Open  2012;2(1):e000506.
WHO case management algorithm for paediatric pneumonia relies solely on symptoms of shortness of breath or cough and tachypnoea for treatment and has poor diagnostic specificity, tends to increase antibiotic resistance. Alternatives, including oxygen saturation measurement, chest ultrasound and chest auscultation, exist but with potential disadvantages. Electronic auscultation has potential for improved detection of paediatric pneumonia but has yet to be standardised. The authors aim to investigate the use of electronic auscultation to improve the specificity of the current WHO algorithm in developing countries.
This study is designed to test the hypothesis that pulmonary pathology can be differentiated from normal using computerised lung sound analysis (CLSA). The authors will record lung sounds from 600 children aged ≤5 years, 100 each with consolidative pneumonia, diffuse interstitial pneumonia, asthma, bronchiolitis, upper respiratory infections and normal lungs at a children's hospital in Lima, Peru. The authors will compare CLSA with the WHO algorithm and other detection approaches, including physical exam findings, chest ultrasound and microbiologic testing to construct an improved algorithm for pneumonia diagnosis.
This study will develop standardised methods for electronic auscultation and chest ultrasound and compare their utility for detection of pneumonia to standard approaches. Utilising signal processing techniques, the authors aim to characterise lung sounds and through machine learning, develop a classification system to distinguish pathologic sounds. Data will allow a better understanding of the benefits and limitations of novel diagnostic techniques in paediatric pneumonia.
Article summary
Article focus
We seek to characterise lung sounds associated with different respiratory illnesses in children using electronic auscultation and determine whether these sounds can be differentiated from normal through computerised lung sound analysis.
We summarise the study design and methods with standardised protocols for electronic auscultation and chest ultrasound in children.
Key message
We aim to develop a protocol for increased specificity of paediatric pneumonia diagnosis in developing countries.
Strengths and limitations of this study
Our study is limited by the case definitions available. With no gold standard for many paediatric respiratory diseases, we will rely on clinical exam findings and chest radiography.
By investigating a number of novel and commonly used diagnostic tools for a variety of respiratory diseases in children, we will gain valuable information regarding the diagnostic potential of each, with a main focus on the electronic stethoscope.
PMCID: PMC3274713  PMID: 22307098
10.  Time-domain analysis of heart sound intensity in children with and without pulmonary artery hypertension: a pilot study using a digital stethoscope 
Pulmonary Circulation  2014;4(4):685-695.
We studied digital stethoscope recordings in children undergoing simultaneous catheterization of the pulmonary artery (PA) to determine whether time-domain analysis of heart sound intensity would aid in the diagnosis of PA hypertension (PAH). Heart sounds were recorded and stored in .wav mono audio format. We performed recordings for 20 seconds with sampling frequencies of 4,000 Hz at the second left intercostal space and the cardiac apex. We used programs written in the MATLAB 2010b environment to analyze signals. We annotated events representing the first (S1) and second (S2) heart sounds and the aortic (A2) and pulmonary (P2) components of S2. We calculated the intensity (I) of the extracted event area (x) as , where n is the total number of heart sound samples in the extracted event and k is A2, P2, S1, or S2. We defined PAH as mean PA pressure (mPAp) of at least 25 mmHg with PA wedge pressure of less than 15 mmHg. We studied 22 subjects (median age: 6 years [range: 0.25–19 years], 13 female), 11 with PAH (median mPAp: 55 mmHg [range: 25–97 mmHg]) and 11 without PAH (median mPAp: 15 mmHg [range: 8–24 mmHg]). The P2∶A2 (P = .0001) and P2∶S2 (P = .0001) intensity ratios were significantly different between subjects with and those without PAH. There was a linear correlation (r > 0.7) between the P2∶S2 and P2∶A2 intensity ratios and mPAp. We found that the P2∶A2 and P2∶S2 intensity ratios discriminated between children with and those without PAH. These findings may be useful for developing an acoustic device to diagnose PAH.
PMCID: PMC4278628  PMID: 25610604
auscultation; second heart sound; phonocardiography; machine learning
11.  Early Classification of Pathological Heartbeats on Wireless Body Sensor Nodes 
Sensors (Basel, Switzerland)  2014;14(12):22532-22551.
Smart Wireless Body Sensor Nodes (WBSNs) are a novel class of unobtrusive, battery-powered devices allowing the continuous monitoring and real-time interpretation of a subject's bio-signals, such as the electrocardiogram (ECG). These low-power platforms, while able to perform advanced signal processing to extract information on heart conditions, are usually constrained in terms of computational power and transmission bandwidth. It is therefore essential to identify in the early stages which parts of an ECG are critical for the diagnosis and, only in these cases, activate on demand more detailed and computationally intensive analysis algorithms. In this work, we present a comprehensive framework for real-time automatic classification of normal and abnormal heartbeats, targeting embedded and resource-constrained WBSNs. In particular, we provide a comparative analysis of different strategies to reduce the heartbeat representation dimensionality, and therefore the required computational effort. We then combine these techniques with a neuro-fuzzy classification strategy, which effectively discerns normal and pathological heartbeats with a minimal run time and memory overhead. We prove that, by performing a detailed analysis only on the heartbeats that our classifier identifies as abnormal, a WBSN system can drastically reduce its overall energy consumption. Finally, we assess the choice of neuro-fuzzy classification by comparing its performance and workload with respect to other state-of-the-art strategies. Experimental results using the MIT-BIH Arrhythmia database show energy savings of as much as 60% in the signal processing stage, and 63% in the subsequent wireless transmission, when a neuro-fuzzy classification structure is employed, coupled with a dimensionality reduction technique based on random projections.
PMCID: PMC4299026  PMID: 25436654
embedded signal processing; wireless body sensor nodes; electrocardiogram; classification
12.  Validation of computerized wheeze detection in young infants during the first months of life 
BMC Pediatrics  2014;14:257.
Several respiratory diseases are associated with specific respiratory sounds. In contrast to auscultation, computerized lung sound analysis is objective and can be performed continuously over an extended period. Moreover, audio recordings can be stored. Computerized lung sounds have rarely been assessed in neonates during the first year of life. This study was designed to determine and validate optimal cut-off values for computerized wheeze detection, based on the assessment by trained clinicians of stored records of lung sounds, in infants aged <1 year.
Lung sounds in 120 sleeping infants, of median (interquartile range) postmenstrual age of 51 (44.5–67.5) weeks, were recorded on 144 test occasions by an automatic wheeze detection device (PulmoTrack®). The records were retrospectively evaluated by three trained clinicians blinded to the results. Optimal cut-off values for the automatically determined relative durations of inspiratory and expiratory wheezing were determined by receiver operating curve analysis, and sensitivity and specificity were calculated.
The optimal cut-off values for the automatically detected durations of inspiratory and expiratory wheezing were 2% and 3%, respectively. These cutoffs had a sensitivity and specificity of 85.7% and 80.7%, respectively, for inspiratory wheezing and 84.6% and 82.5%, respectively, for expiratory wheezing. Inter-observer reliability among the experts was moderate, with a Fleiss’ Kappa (95% confidence interval) of 0.59 (0.57-0.62) for inspiratory and 0.54 (0.52 - 0.57) for expiratory wheezing.
Computerized wheeze detection is feasible during the first year of life. This method is more objective and can be more readily standardized than subjective auscultation, providing quantitative and noninvasive information about the extent of wheezing.
PMCID: PMC4287542  PMID: 25296955
Lung sound; Auscultation; Phonopneumography; Wheezing; Computerized wheeze detection; Infants
13.  Auditory selective attention is enhanced by a task-irrelevant temporally coherent visual stimulus in human listeners 
eLife  null;4:e04995.
In noisy settings, listening is aided by correlated dynamic visual cues gleaned from a talker's face—an improvement often attributed to visually reinforced linguistic information. In this study, we aimed to test the effect of audio–visual temporal coherence alone on selective listening, free of linguistic confounds. We presented listeners with competing auditory streams whose amplitude varied independently and a visual stimulus with varying radius, while manipulating the cross-modal temporal relationships. Performance improved when the auditory target's timecourse matched that of the visual stimulus. The fact that the coherence was between task-irrelevant stimulus features suggests that the observed improvement stemmed from the integration of auditory and visual streams into cross-modal objects, enabling listeners to better attend the target. These findings suggest that in everyday conditions, where listeners can often see the source of a sound, temporal cues provided by vision can help listeners to select one sound source from a mixture.
eLife digest
In the noisy din of a cocktail party, there are many sources of sound that compete for our attention. Even so, we can easily block out the noise and focus on a conversation, especially when we are talking to someone in front of us.
This is possible in part because our sensory system combines inputs from our senses. Scientists have proposed that our perception is stronger when we can hear and see something at the same time, as opposed to just being able to hear it. For example, if we tried to talk to someone on a phone during a cocktail party, the background noise would probably drown out the conversation. However, when we can see the person we are talking to, it is easier to hold a conversation.
Maddox et al. have now explored this phenomenon in experiments that involved human subjects listening to an audio stream that was masked by background sound. While listening, the subjects also watched completely irrelevant videos that moved in sync with either the audio stream or with the background sound. The subjects then had to perform a task that involved pushing a button when they heard random changes (such as subtle changes in tone or pitch) in the audio stream.
The experiment showed that the subjects performed well when they saw a video that was in sync with the audio stream. However, their performance dropped when the video was in sync with the background sound. This suggests that when we hold a conversation during a noisy cocktail party, seeing the other person's face move as they talk creates a combined audio–visual impression of that person, helping us separate what they are saying from all the noise in the background. However, if we turn to look at other guests, we become distracted and the conversation may become lost.
PMCID: PMC4337603  PMID: 25654748
auditory-visual integration; auditory scene analysis; multisensory; selective attention; temporal coherence; human
14.  An open real-time tele-stethoscopy system 
Acute respiratory infections are the leading cause of childhood mortality. The lack of physicians in rural areas of developing countries makes difficult their correct diagnosis and treatment. The staff of rural health facilities (health-care technicians) may not be qualified to distinguish respiratory diseases by auscultation. For this reason, the goal of this project is the development of a tele-stethoscopy system that allows a physician to receive real-time cardio-respiratory sounds from a remote auscultation, as well as video images showing where the technician is placing the stethoscope on the patient’s body.
A real-time wireless stethoscopy system was designed. The initial requirements were: 1) The system must send audio and video synchronously over IP networks, not requiring an Internet connection; 2) It must preserve the quality of cardiorespiratory sounds, allowing to adapt the binaural pieces and the chestpiece of standard stethoscopes, and; 3) Cardiorespiratory sounds should be recordable at both sides of the communication. In order to verify the diagnostic capacity of the system, a clinical validation with eight specialists has been designed. In a preliminary test, twelve patients have been auscultated by all the physicians using the tele-stethoscopy system, versus a local auscultation using traditional stethoscope. The system must allow listen the cardiac (systolic and diastolic murmurs, gallop sound, arrhythmias) and respiratory (rhonchi, rales and crepitations, wheeze, diminished and bronchial breath sounds, pleural friction rub) sounds.
The design, development and initial validation of the real-time wireless tele-stethoscopy system are described in detail. The system was conceived from scratch as open-source, low-cost and designed in such a way that many universities and small local companies in developing countries may manufacture it. Only free open-source software has been used in order to minimize manufacturing costs and look for alliances to support its improvement and adaptation. The microcontroller firmware code, the computer software code and the PCB schematics are available for free download in a subversion repository hosted in SourceForge.
It has been shown that real-time tele-stethoscopy, together with a videoconference system that allows a remote specialist to oversee the auscultation, may be a very helpful tool in rural areas of developing countries.
PMCID: PMC3499164  PMID: 22917062
Telemedicine; Stethoscope; Tele-stethoscopy; Wireless; Real-time; E-health; Libre software; Libre hardware; Open-source
15.  Intelligent Classification of Heartbeats for Automated Real-Time ECG Monitoring 
Telemedicine Journal and e-Health  2014;20(12):1069-1077.
Background: The automatic interpretation of electrocardiography (ECG) data can provide continuous analysis of heart activity, allowing the effective use of wireless devices such as the Holter monitor. Materials and Methods: We propose an intelligent heartbeat monitoring system to detect the possibility of arrhythmia in real time. We detected heartbeats and extracted features such as the QRS complex and P wave from ECG signals using the Pan–Tompkins algorithm, and the heartbeats were then classified into 16 types using a decision tree. Results: We tested the sensitivity, specificity, and accuracy of our system against data from the MIT-BIH Arrhythmia Database. Our system achieved an average accuracy of 97% in heartbeat detection and an average heartbeat classification accuracy of above 96%, which is comparable with the best competing schemes. Conclusions: This work provides a guide to the systematic design of an intelligent classification system for decision support in Holter ECG monitoring.
PMCID: PMC4270110  PMID: 25010717
heartbeat detection; heartbeat classification; decision tree; electrocardiography monitoring
16.  Establishing a gold standard for manual cough counting: video versus digital audio recordings 
Manual cough counting is time-consuming and laborious; however it is the standard to which automated cough monitoring devices must be compared. We have compared manual cough counting from video recordings with manual cough counting from digital audio recordings.
We studied 8 patients with chronic cough, overnight in laboratory conditions (diagnoses were 5 asthma, 1 rhinitis, 1 gastro-oesophageal reflux disease and 1 idiopathic cough). Coughs were recorded simultaneously using a video camera with infrared lighting and digital sound recording.
The numbers of coughs in each 8 hour recording were counted manually, by a trained observer, in real time from the video recordings and using audio-editing software from the digital sound recordings.
The median cough frequency was 17.8 (IQR 5.9–28.7) cough sounds per hour in the video recordings and 17.7 (6.0–29.4) coughs per hour in the digital sound recordings. There was excellent agreement between the video and digital audio cough rates; mean difference of -0.3 coughs per hour (SD ± 0.6), 95% limits of agreement -1.5 to +0.9 coughs per hour. Video recordings had poorer sound quality even in controlled conditions and can only be analysed in real time (8 hours per recording). Digital sound recordings required 2–4 hours of analysis per recording.
Manual counting of cough sounds from digital audio recordings has excellent agreement with simultaneous video recordings in laboratory conditions. We suggest that ambulatory digital audio recording is therefore ideal for validating future cough monitoring devices, as this as this can be performed in the patients own environment.
PMCID: PMC1557531  PMID: 16887019
17.  A decision tree – based method for the differential diagnosis of Aortic Stenosis from Mitral Regurgitation using heart sounds 
New technologies like echocardiography, color Doppler, CT, and MRI provide more direct and accurate evidence of heart disease than heart auscultation. However, these modalities are costly, large in size and operationally complex and therefore are not suitable for use in rural areas, in homecare and generally in primary healthcare set-ups. Furthermore the majority of internal medicine and cardiology training programs underestimate the value of cardiac auscultation and junior clinicians are not adequately trained in this field. Therefore efficient decision support systems would be very useful for supporting clinicians to make better heart sound diagnosis. In this study a rule-based method, based on decision trees, has been developed for differential diagnosis between "clear" Aortic Stenosis (AS) and "clear" Mitral Regurgitation (MR) using heart sounds.
For the purposes of our experiment we used a collection of 84 heart sound signals including 41 heart sound signals with "clear" AS systolic murmur and 43 with "clear" MR systolic murmur. Signals were initially preprocessed to detect 1st and 2nd heart sounds. Next a total of 100 features were determined for every heart sound signal and relevance to the differentiation between AS and MR was estimated. The performance of fully expanded decision tree classifiers and Pruned decision tree classifiers were studied based on various training and test datasets. Similarly, pruned decision tree classifiers were used to examine their differentiation capabilities. In order to build a generalized decision support system for heart sound diagnosis, we have divided the problem into sub problems, dealing with either one morphological characteristic of the heart-sound waveform or with difficult to distinguish cases.
Relevance analysis on the different heart sound features demonstrated that the most relevant features are the frequency features and the morphological features that describe S1, S2 and the systolic murmur. The results are compatible with the physical understanding of the problem since AS and MR systolic murmurs have different frequency contents and different waveform shapes. On the contrary, in the diastolic phase there is no murmur in both diseases which results in the fact that the diastolic phase signals cannot contribute to the differentiation between AS and MR.
We used a fully expanded decision tree classifier with a training set of 34 records and a test set of 50 records which resulted in a classification accuracy (total corrects/total tested) of 90% (45 correct/50 total records). Furthermore, the method proved to correctly classify both AS and MR cases since the partial AS and MR accuracies were 91.6% and 88.5% respectively. Similar accuracy was achieved using decision trees with a fraction of the 100 features (the most relevant). Pruned Differentiation decision trees did not significantly change the classification accuracy of the decision trees both in terms of partial classification and overall classification as well.
Present work has indicated that decision tree algorithms decision tree algorithms can be successfully used as a basis for a decision support system to assist young and inexperienced clinicians to make better heart sound diagnosis. Furthermore, Relevance Analysis can be used to determine a small critical subset, from the initial set of features, which contains most of the information required for the differentiation. Decision tree structures, if properly trained can increase their classification accuracy in new test data sets. The classification accuracy and the generalization capabilities of the Fully Expanded decision tree structures and the Pruned decision tree structures have not significant difference for this examined sub-problem. However, the generalization capabilities of the decision tree based methods were found to be satisfactory. Decision tree structures were tested on various training and test data set and the classification accuracy was found to be consistently high.
PMCID: PMC481080  PMID: 15225347
18.  Evaluation of BioCreAtIvE assessment of task 2 
BMC Bioinformatics  2005;6(Suppl 1):S16.
Molecular Biology accumulated substantial amounts of data concerning functions of genes and proteins. Information relating to functional descriptions is generally extracted manually from textual data and stored in biological databases to build up annotations for large collections of gene products. Those annotation databases are crucial for the interpretation of large scale analysis approaches using bioinformatics or experimental techniques. Due to the growing accumulation of functional descriptions in biomedical literature the need for text mining tools to facilitate the extraction of such annotations is urgent. In order to make text mining tools useable in real world scenarios, for instance to assist database curators during annotation of protein function, comparisons and evaluations of different approaches on full text articles are needed.
The Critical Assessment for Information Extraction in Biology (BioCreAtIvE) contest consists of a community wide competition aiming to evaluate different strategies for text mining tools, as applied to biomedical literature. We report on task two which addressed the automatic extraction and assignment of Gene Ontology (GO) annotations of human proteins, using full text articles. The predictions of task 2 are based on triplets of protein – GO term – article passage. The annotation-relevant text passages were returned by the participants and evaluated by expert curators of the GO annotation (GOA) team at the European Institute of Bioinformatics (EBI). Each participant could submit up to three results for each sub-task comprising task 2. In total more than 15,000 individual results were provided by the participants. The curators evaluated in addition to the annotation itself, whether the protein and the GO term were correctly predicted and traceable through the submitted text fragment.
Concepts provided by GO are currently the most extended set of terms used for annotating gene products, thus they were explored to assess how effectively text mining tools are able to extract those annotations automatically. Although the obtained results are promising, they are still far from reaching the required performance demanded by real world applications. Among the principal difficulties encountered to address the proposed task, were the complex nature of the GO terms and protein names (the large range of variants which are used to express proteins and especially GO terms in free text), and the lack of a standard training set. A range of very different strategies were used to tackle this task. The dataset generated in line with the BioCreative challenge is publicly available and will allow new possibilities for training information extraction methods in the domain of molecular biology.
PMCID: PMC1869008  PMID: 15960828
19.  Music in Our Ears: The Biological Bases of Musical Timbre Perception 
PLoS Computational Biology  2012;8(11):e1002759.
Timbre is the attribute of sound that allows humans and other animals to distinguish among different sound sources. Studies based on psychophysical judgments of musical timbre, ecological analyses of sound's physical characteristics as well as machine learning approaches have all suggested that timbre is a multifaceted attribute that invokes both spectral and temporal sound features. Here, we explored the neural underpinnings of musical timbre. We used a neuro-computational framework based on spectro-temporal receptive fields, recorded from over a thousand neurons in the mammalian primary auditory cortex as well as from simulated cortical neurons, augmented with a nonlinear classifier. The model was able to perform robust instrument classification irrespective of pitch and playing style, with an accuracy of 98.7%. Using the same front end, the model was also able to reproduce perceptual distance judgments between timbres as perceived by human listeners. The study demonstrates that joint spectro-temporal features, such as those observed in the mammalian primary auditory cortex, are critical to provide the rich-enough representation necessary to account for perceptual judgments of timbre by human listeners, as well as recognition of musical instruments.
Author Summary
Music is a complex acoustic experience that we often take for granted. Whether sitting at a symphony hall or enjoying a melody over earphones, we have no difficulty identifying the instruments playing, following various beats, or simply distinguishing a flute from an oboe. Our brains rely on a number of sound attributes to analyze the music in our ears. These attributes can be straightforward like loudness or quite complex like the identity of the instrument. A major contributor to our ability to recognize instruments is what is formally called ‘timbre’. Of all perceptual attributes of music, timbre remains the most mysterious and least amenable to a simple mathematical abstraction. In this work, we examine the neural underpinnings of musical timbre in an attempt to both define its perceptual space and explore the processes underlying timbre-based recognition. We propose a scheme based on responses observed at the level of mammalian primary auditory cortex and show that it can accurately predict sound source recognition and perceptual timbre judgments by human listeners. The analyses presented here strongly suggest that rich representations such as those observed in auditory cortex are critical in mediating timbre percepts.
PMCID: PMC3486808  PMID: 23133363
20.  Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning 
PeerJ  2014;2:e488.
Automatic species classification of birds from their sound is a computational tool of increasing importance in ecology, conservation monitoring and vocal communication studies. To make classification useful in practice, it is crucial to improve its accuracy while ensuring that it can run at big data scales. Many approaches use acoustic measures based on spectrogram-type data, such as the Mel-frequency cepstral coefficient (MFCC) features which represent a manually-designed summary of spectral information. However, recent work in machine learning has demonstrated that features learnt automatically from data can often outperform manually-designed feature transforms. Feature learning can be performed at large scale and “unsupervised”, meaning it requires no manual data labelling, yet it can improve performance on “supervised” tasks such as classification. In this work we introduce a technique for feature learning from large volumes of bird sound recordings, inspired by techniques that have proven useful in other domains. We experimentally compare twelve different feature representations derived from the Mel spectrum (of which six use this technique), using four large and diverse databases of bird vocalisations, classified using a random forest classifier. We demonstrate that in our classification tasks, MFCCs can often lead to worse performance than the raw Mel spectral data from which they are derived. Conversely, we demonstrate that unsupervised feature learning provides a substantial boost over MFCCs and Mel spectra without adding computational complexity after the model has been trained. The boost is particularly notable for single-label classification tasks at large scale. The spectro-temporal activations learned through our procedure resemble spectro-temporal receptive fields calculated from avian primary auditory forebrain. However, for one of our datasets, which contains substantial audio data but few annotations, increased performance is not discernible. We study the interaction between dataset characteristics and choice of feature representation through further empirical analysis.
PMCID: PMC4106198  PMID: 25083350
Bioacoustics; Machine learning; Birds; Classification; Vocalisation; Birdsong
21.  Automatic Detection of Whole Night Snoring Events Using Non-Contact Microphone 
PLoS ONE  2013;8(12):e84139.
Although awareness of sleep disorders is increasing, limited information is available on whole night detection of snoring. Our study aimed to develop and validate a robust, high performance, and sensitive whole-night snore detector based on non-contact technology.
Sounds during polysomnography (PSG) were recorded using a directional condenser microphone placed 1 m above the bed. An AdaBoost classifier was trained and validated on manually labeled snoring and non-snoring acoustic events.
Sixty-seven subjects (age 52.5±13.5 years, BMI 30.8±4.7 kg/m2, m/f 40/27) referred for PSG for obstructive sleep apnea diagnoses were prospectively and consecutively recruited. Twenty-five subjects were used for the design study; the validation study was blindly performed on the remaining forty-two subjects.
Measurements and Results
To train the proposed sound detector, >76,600 acoustic episodes collected in the design study were manually classified by three scorers into snore and non-snore episodes (e.g., bedding noise, coughing, environmental). A feature selection process was applied to select the most discriminative features extracted from time and spectral domains. The average snore/non-snore detection rate (accuracy) for the design group was 98.4% based on a ten-fold cross-validation technique. When tested on the validation group, the average detection rate was 98.2% with sensitivity of 98.0% (snore as a snore) and specificity of 98.3% (noise as noise).
Audio-based features extracted from time and spectral domains can accurately discriminate between snore and non-snore acoustic events. This audio analysis approach enables detection and analysis of snoring sounds from a full night in order to produce quantified measures for objective follow-up of patients.
PMCID: PMC3877189  PMID: 24391903
22.  Zipf's Law in Short-Time Timbral Codings of Speech, Music, and Environmental Sound Signals 
PLoS ONE  2012;7(3):e33993.
Timbre is a key perceptual feature that allows discrimination between different sounds. Timbral sensations are highly dependent on the temporal evolution of the power spectrum of an audio signal. In order to quantitatively characterize such sensations, the shape of the power spectrum has to be encoded in a way that preserves certain physical and perceptual properties. Therefore, it is common practice to encode short-time power spectra using psychoacoustical frequency scales. In this paper, we study and characterize the statistical properties of such encodings, here called timbral code-words. In particular, we report on rank-frequency distributions of timbral code-words extracted from 740 hours of audio coming from disparate sources such as speech, music, and environmental sounds. Analogously to text corpora, we find a heavy-tailed Zipfian distribution with exponent close to one. Importantly, this distribution is found independently of different encoding decisions and regardless of the audio source. Further analysis on the intrinsic characteristics of most and least frequent code-words reveals that the most frequent code-words tend to have a more homogeneous structure. We also find that speech and music databases have specific, distinctive code-words while, in the case of the environmental sounds, this database-specific code-words are not present. Finally, we find that a Yule-Simon process with memory provides a reasonable quantitative approximation for our data, suggesting the existence of a common simple generative mechanism for all considered sound sources.
PMCID: PMC3315504  PMID: 22479497
23.  Probing the Interoceptive Network by Listening to Heartbeats: An fMRI Study 
PLoS ONE  2015;10(7):e0133164.
Exposure to cues of homeostatic relevance (i.e. heartbeats) is supposed to increase the allocation of attentional resources towards the cue, due to its importance for self-regulatory, interoceptive processes. This functional magnetic resonance imaging (fMRI) study aimed at determining whether listening to heartbeats is accompanied by activation in brain areas associated with interoception, particularly the insular cortex. Brain activity was measured with fMRI during cue-exposure in 36 subjects while listening to heartbeats vs. sinus tones. Autonomic markers (skin conductance) and subjective measures of state and trait anxiety were assessed. Stimulation with heartbeat sounds triggered activation in brain areas commonly associated with the processing of interoceptive information, including bilateral insular cortices, the inferior frontal operculum, and the middle frontal gyrus. A psychophysiological interaction analysis indicated a functional connectivity between the middle frontal gyrus (seed region) and bilateral insular cortices, the left amygdala and the supplementary motor area. The magnitude of neural activation in the right anterior insular cortex was positively associated with autonomic arousal. The present findings indicate that listening to heartbeats induced activity in areas of the interoception network as well as changes in psychophysiological arousal and subjective emotional experience. As this approach constitutes a promising method for studying interoception in the fMRI environment, a clinical application in anxiety prone populations should be addressed by future studies.
PMCID: PMC4512728  PMID: 26204524
24.  Spectral analysis of bowel sounds in intestinal obstruction using an electronic stethoscope 
AIM: To determine the value of bowel sounds analysis using an electronic stethoscope to support a clinical diagnosis of intestinal obstruction.
METHODS: Subjects were patients who presented with a diagnosis of possible intestinal obstruction based on symptoms, signs, and radiological findings. A 3M™ Littmann® Model 4100 electronic stethoscope was used in this study. With the patients lying supine, six 8-second recordings of bowel sounds were taken from each patient from the lower abdomen. The recordings were analysed for sound duration, sound-to-sound interval, dominant frequency, and peak frequency. Clinical and radiological data were reviewed and the patients were classified as having either acute, subacute, or no bowel obstruction. Comparison of bowel sound characteristics was made between these subgroups of patients. In the presence of an obstruction, the site of obstruction was identified and bowel calibre was also measured to correlate with bowel sounds.
RESULTS: A total of 71 patients were studied during the period July 2009 to January 2011. Forty patients had acute bowel obstruction (27 small bowel obstruction and 13 large bowel obstruction), 11 had subacute bowel obstruction (eight in the small bowel and three in large bowel) and 20 had no bowel obstruction (diagnoses of other conditions were made). Twenty-five patients received surgical intervention (35.2%) during the same admission for acute abdominal conditions. A total of 426 recordings were made and 420 recordings were used for analysis. There was no significant difference in sound-to-sound interval, dominant frequency, and peak frequency among patients with acute bowel obstruction, subacute bowel obstruction, and no bowel obstruction. In acute large bowel obstruction, the sound duration was significantly longer (median 0.81 s vs 0.55 s, P = 0.021) and the dominant frequency was significantly higher (median 440 Hz vs 288 Hz, P = 0.003) when compared to acute small bowel obstruction. No significant difference was seen between acute large bowel obstruction and large bowel pseudo-obstruction. For patients with small bowel obstruction, the sound-to-sound interval was significantly longer in those who subsequently underwent surgery compared with those treated non-operatively (median 1.29 s vs 0.63 s, P < 0.001). There was no correlation between bowel calibre and bowel sound characteristics in both acute small bowel obstruction and acute large bowel obstruction.
CONCLUSION: Auscultation of bowel sounds is non-specific for diagnosing bowel obstruction. Differences in sound characteristics between large bowel and small bowel obstruction may help determine the likely site of obstruction.
PMCID: PMC3435785  PMID: 22969233
Bowel sounds; Intestinal obstruction; Spectral analysis; Electronic stethoscope
25.  Human Neuromagnetic Steady-State Responses to Amplitude-Modulated Tones, Speech, and Music 
Ear and Hearing  2014;35(4):461-467.
Auditory steady-state responses that can be elicited by various periodic sounds inform about subcortical and early cortical auditory processing. Steady-state responses to amplitude-modulated pure tones have been used to scrutinize binaural interaction by frequency-tagging the two ears’ inputs at different frequencies. Unlike pure tones, speech and music are physically very complex, as they include many frequency components, pauses, and large temporal variations. To examine the utility of magnetoencephalographic (MEG) steady-state fields (SSFs) in the study of early cortical processing of complex natural sounds, the authors tested the extent to which amplitude-modulated speech and music can elicit reliable SSFs.
MEG responses were recorded to 90-s-long binaural tones, speech, and music, amplitude-modulated at 41.1 Hz at four different depths (25, 50, 75, and 100%). The subjects were 11 healthy, normal-hearing adults. MEG signals were averaged in phase with the modulation frequency, and the sources of the resulting SSFs were modeled by current dipoles. After the MEG recording, intelligibility of the speech, musical quality of the music stimuli, naturalness of music and speech stimuli, and the perceived deterioration caused by the modulation were evaluated on visual analog scales.
The perceived quality of the stimuli decreased as a function of increasing modulation depth, more strongly for music than speech; yet, all subjects considered the speech intelligible even at the 100% modulation. SSFs were the strongest to tones and the weakest to speech stimuli; the amplitudes increased with increasing modulation depth for all stimuli. SSFs to tones were reliably detectable at all modulation depths (in all subjects in the right hemisphere, in 9 subjects in the left hemisphere) and to music stimuli at 50 to 100% depths, whereas speech usually elicited clear SSFs only at 100% depth.
The hemispheric balance of SSFs was toward the right hemisphere for tones and speech, whereas SSFs to music showed no lateralization. In addition, the right lateralization of SSFs to the speech stimuli decreased with decreasing modulation depth.
The results showed that SSFs can be reliably measured to amplitude-modulated natural sounds, with slightly different hemispheric lateralization for different carrier sounds. With speech stimuli, modulation at 100% depth is required, whereas for music the 75% or even 50% modulation depths provide a reasonable compromise between the signal-to-noise ratio of SSFs and sound quality or perceptual requirements. SSF recordings thus seem feasible for assessing the early cortical processing of natural sounds.
Auditory steady state responses to pure tones have been used to study subcortical and cortical processing, to scrutinize binaural interaction, and to evaluate hearing in an objective way. In daily lives, sounds that are physically much more complex sounds are encountered, such as music and speech. This study demonstrates that not only pure tones but also amplitude-modulated speech and music, both perceived to have tolerable sound quality, can elicit reliable magnetoencephalographic steady state fields. The strengths and hemispheric lateralization of the responses differed between the carrier sounds. The results indicate that steady state responses could be used to study the early cortical processing of natural sounds.
PMCID: PMC4072443  PMID: 24603544
Amplitude modulation; Auditory; Frequency tagging; Magnetoencephalography; Natural stimuli

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