A robust heart sounds segmentation module based on S-transform

This paper presents a new module for heart sounds segmentation based on S-transform. The heart sounds segmentation process segments the PhonoCardioGram (PCG) signal into four parts: S1 (first heart sound), systole, S2 (second heart sound) and diastole. It can be considered one of the most important phases in the auto-analysis of PCG signals. The proposed segmentation module can be divided into three main blocks: localization of heart sounds, boundaries detection of the localized heart sounds and classification block to distinguish between S1 and S2. An original localization method of heart sounds are proposed in this study. The method named SSE calculates the Shannon energy of the local spectrum calculated by the S-transform for each sample of the heart sound signal. The second block contains a novel approach for the boundaries detection of S1 and S2. The energy concentrations of the S-transform of localized sounds are optimized by using a window width optimization algorithm. Then the SSE envelope is recalculated and a local adaptive threshold is applied to refine the estimated boundaries. To distinguish between S1 and S2, a feature extraction method based on the singular value decomposition (SVD) of the S-matrix is applied in this study. The proposed segmentation module is evaluated at each block according to a database of 80 sounds, including 40 sounds with cardiac pathologies.     

Heart energy signature spectrogram for cardiovascular diagnosis

A new method and application is proposed to characterize intensity and pitch of human heart sounds and murmurs. Using recorded heart sounds from the library of one of the authors, a visual map of heart sound energy was established. Both normal and abnormal heart sound recordings were studied. Representation is based on Wigner-Ville joint time-frequency transformations. The proposed methodology separates acoustic contributions of cardiac events simultaneously in pitch, time and energy. The resolution accuracy is superior to any other existing spectrogram method. The characteristic energy signature of the innocent heart murmur in a child with the S3 sound is presented. It allows clear detection of S1, S2 and S3 sounds, S2 split, systolic murmur, and intensity of these components. The original signal, heart sound power change with time, time-averaged frequency, energy density spectra and instantaneous variations of power and frequency/pitch with time, are presented. These data allow full quantitative characterization of heart sounds and murmurs. High accuracy in both time and pitch resolution is demonstrated. Resulting visual images have self-referencing quality, whereby individual features and their changes become immediately obvious.     

Measuring the heart rate of the spider, Aphonopelma hentzi: a non-invasive technique

In this work we describe a non‐invasive and precise technique to record the heartbeats of a spider. A linear output Hall effect transducer in conjunction with a small magnet was used to monitor the micromovements on the dorsal surface of the abdomen of the tarantula Aphonopelma hentzi (Girard) (Theraphosidae). The exoskeleton in this region is in direct contact with suspensory ligaments connected to the heart, and the dorsal cuticle of the opisthosoma moves with each heartbeat. The technique allowed the discrimination of the different stages of the spider's cardiac cycle. The method can be also adapted for a smaller spider or other arthropods. We believe that the method proposed in this paper allows investigators to gain insights into a spider's natural heart rate by gathering unbiased data with a non‐invasive and very precise technique. We have found the resting heart rate of A. hentzi to be 5.6 ± 1.47 beats/min, which is lower than previously reported values.     

一种袖珍式胎心音仪的设计和实现

研制出一种简单实用、安全可靠并具有广阔市场前景的袖珍式胎心音仪。该仪器利用超声多普勒原理,用超声探头从母体腹部提取胎儿心音信号,经过信号解调及低频放大、音频功率放大等处理,然后由耳机及扬声器输出胎心音。     

Temporal separation of whale vocalizations from background oceanic noise using a power calculation

The process of analyzing audio signals in search of cetacean vocalizations is in many cases a very arduous task, requiring many complex computations, a plethora of digital processing techniques and the scrutinization of an audio signal with a fine comb to determine where the vocalizations are located. To ease this process, a computationally efficient and noise-resistant method for determining whether an audio segment contains a potential cetacean call is developed here with the help of a robust power calculation for stationary Gaussian noise signals and a recursive method for determining the mean and variance of a given sample frame. The resulting detector is tested on audio recordings containing southern right whale sounds and its performance is compared to a contemporary energy detector and a popular deep learning method. The detector exhibits good performance at moderate-to-high signal-to-noise ratio values. The detector succeeds in being easy to implement, computationally efficient to use and robust enough to accurately detect whale vocalizations in a noisy underwater environment.     

A System for Heart Sounds Classification

The future of quick and efficient disease diagnosis lays in the development of reliable non-invasive methods. As for the cardiac diseases – one of the major causes of death around the globe – a concept of an electronic stethoscope equipped with an automatic heart tone identification system appears to be the best solution. Thanks to the advancement in technology, the quality of phonocardiography signals is no longer an issue. However, appropriate algorithms for auto-diagnosis systems of heart diseases that could be capable of distinguishing most of known pathological states have not been yet developed. The main issue is non-stationary character of phonocardiography signals as well as a wide range of distinguishable pathological heart sounds. In this paper a new heart sound classification technique, which might find use in medical diagnostic systems, is presented. It is shown that by combining Linear Predictive Coding coefficients, used for future extraction, with a classifier built upon combining Support Vector Machine and Modified Cuckoo Search algorithm, an improvement in performance of the diagnostic system, in terms of accuracy, complexity and range of distinguishable heart sounds, can be made. The developed system achieved accuracy above 93% for all considered cases including simultaneous identification of twelve different heart sound classes. The respective system is compared with four different major classification methods, proving its reliability.     

Heart‐beat‐phase‐coherent Doppler optical coherence tomography for measuring pulsatile ocular blood flow

We introduce a Doppler OCT (DOCT) platform that is fully synchronized with the heart‐beat via a pulse oximeter. The system allows reconstructing heart‐beat‐phase‐coherent quantitative DOCT volumes. The method is to acquire a series of DOCT volumes and to record the pulse in parallel. The heartbeat data is used for triggering the start of each DOCT volume acquisition. The recorded volume series is registered to the level of capillaries using a cross‐volume registration. The information of the pulse phase is used to rearrange the tomograms in time, to obtain a series of phase coherent DOCT volumes over a pulse. We present Doppler angle independent quantitative evaluation of the absolute pulsatile blood flow within individual retinal vessels as well as of the total retinal blood flow over a full heartbeat cycle. (© 2013 WILEY‐VCH Verlag GmbH & Co. KGaA, Weinheim)     

Arrhythmic Heartbeat Classification Using 2D Convolutional Neural Networks

BackgroundElectrocardiogram (ECG) is a method of recording the electrical activity of the heart and it provides a diagnostic means for heart-related diseases. Arrhythmia is any irregularity of the heartbeat that causes an abnormality in the heart rhythm. Early detection of arrhythmia has great importance to prevent many diseases. Manual analysis of ECG recordings is not practical for quickly identifying arrhythmias that may cause sudden deaths. Hence, many studies have been presented to develop computer-aided-diagnosis (CAD) systems to automatically identify arrhythmias.MethodsThis paper proposes a novel deep learning approach to identify arrhythmias in ECG signals. The proposed approach identifies arrhythmia classes using Convolutional Neural Network (CNN) trained by two-dimensional (2D) ECG beat images. Firstly, ECG signals, which consist of 5 different arrhythmias, are segmented into heartbeats which are transformed into 2D grayscale images. Afterward, the images are used as input for training a new CNN architecture to classify heartbeats.ResultsThe experimental results show that the classification performance of the proposed approach reaches an overall accuracy of 99.7%, sensitivity of 99.7%, and specificity of 99.22% in the classification of five different ECG arrhythmias. Further, the proposed CNN architecture is compared to other popular CNN architectures such as LeNet and ResNet-50 to evaluate the performance of the study.ConclusionsTest results demonstrate that the deep network trained by ECG images provides outstanding classification performance of arrhythmic ECG signals and outperforms similar network architectures. Moreover, the proposed method has lower computational costs compared to existing methods and is more suitable for mobile device-based diagnosis systems as it does not involve any complex preprocessing process. Hence, the proposed approach provides a simple and robust automatic cardiac arrhythmia detection scheme for the classification of ECG arrhythmias.     

A generating model of realistic synthetic heart sounds for performance assessment of phonocardiogram processing algorithms

A new model which is capable of generating realistic synthetic phonocardiogram (PCG) signals is introduced based on three coupled ordinary differential equations. The new PCG model takes into account the respiratory frequency, the heart rate variability and the time splitting of first and second heart sounds. This time splitting occurs with each cardiac cycle and varies with inhalation and exhalation. Clinical PCG statistics and the close temporal relationship between events in ECG and PCG are used to deduce values of PCG model parameters.In comparison with published PCG models, the proposed model allows a larger number of known PCG features to be taken into consideration. Moreover it is able to generate both normal and abnormal realistic synthetic heart sounds. Results show that these synthetic PCG signals have the closest features to those of a conventional heart sound in both time and frequency domains. Additionally, a sound quality test carried out by eight cardiologists demonstrates that the proposed model outperforms the existing models.This new PCG model is promising and useful in assessing signal processing techniques which are developed to help clinical diagnosis based on PCG.     

Two Statistical Methods for Resolving Healthy Individuals and Those with Congestive Heart Failure Based on Extended Self-similarity and a Recursive Method

In this paper we introduce two methods for measuring irregularities in human heartbeat time series (HHTS). First we consider the multi-fractal structure of HHTS to distinguish healthy individuals and from those with congestive heart failure. In this way we modify the Extended Self-Similarity (ESS) method and apply it to HHTS. Our second approach is based on the recursive method, which we use to predict the duration of the next heartbeat by considering a few previous ones. We use standard physiological data and show that these approaches lead to very satisfactory methods to resolve the healthy and CHF individuals. These methods can be used potentially in portable electronic heart alarm systems.     

Comparison of first and second heart sounds after mechanical heart valve replacement

In this article, the spectral features of first heart sounds (S1) and second heart sounds (S2), which comprise the mechanical heart valve sounds obtained after aortic valve replacement (AVR) and mitral valve replacement (MVR), are compared to find out the effect of mechanical heart valve replacement and recording area on S1 and S2. For this aim, the Welch method and the autoregressive (AR) method are applied on the S1 and S2 taken from 66 recordings of 8 patients with AVR and 98 recordings from 11 patients with MVR, thereby yielding power spectrum of the heart sounds. Three features relating to frequency of heart sounds and three features relating to energy of heart sounds are obtained. Results show that in comparison to natural heart valves, mechanical heart valves contain higher frequency components and energy, and energy and frequency components do not show common behaviour for either AVR or MVR depending on the recording areas. Aside from the frequency content and energy of the sound generated by mechanical heart valves being affected by the structure of the lungs–thorax and the recording areas, the pressure across the valve incurred during AVR or MVR is a significant factor in determining the frequency and energy levels of the valve sound produced. Though studies on native heart sounds as a non-invasive diagnostic method has been done for many years, it is observed that studies on mechanical heart valves sounds are limited. The results of this paper will contribute to other studies on using a non-invasive method for assessing the mechanical heart valve sounds.     

Zipf's law in short-time timbral codings of speech, music, and environmental sound signals

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.     

Isolation of Fetal ECG Signals from Abdominal ECG Using Wavelet Analysis

ObjectiveMonitoring the heartbeat of the fetus during pregnancy is a vital part in determining their health. Current fetal heart monitoring techniques lack the accuracy in fetal heart rate monitoring and features acquisition, resulting in diagnostic medical issues. The demand for a reliable method of non-invasive fetal heart monitoring is of high importance.MethodElectrocardiogram (ECG) is a method of monitoring the electrical activity produced by the heart. The extraction of the fetal ECG (FECG) from the abdominal ECG (AECG) is challenging since both ECGs of the mother and the baby share similar frequency components, adding to the fact that the signals are corrupted by white noise. This paper presents a method of FECG extraction by eliminating all other signals using AECG. The algorithm is based on attenuating the maternal ECG (MECG) by filtering and wavelet analysis to find the locations of the FECG, and thus isolating them based on their locations. Two signals of AECG collected at different locations on the abdomens are used. The ECG data used contains MECG of a power of five to ten times that of the FECG.ResultsThe FECG signals were successfully isolated from the AECG using the proposed method through which the QRS complex of the heartbeat was conserved, and heart rate was calculated. The fetal heart rate was 135 bpm and the instantaneous heart rate was 131.58 bpm. The heart rate of the mother was at 90 bpm with an instantaneous heart rate of 81.9 bpm.ConclusionThe proposed method is promising for FECG extraction since it relies on filtering and wavelet analysis of two abdominal signals for the algorithm. The method implemented is easily adjusted based on the power levels of signals, giving it great ease of adaptation to changing signals in different biosignals applications.     

Heartbeat patterns during the postembryonic development of Drosophila melanogaster

Pulsations of the dorsal vessel were recorded in vivo during the whole postembryonic development of D. melanogaster, by means of a newly invented, pulse-light opto-cardiographic method. The young larvae of the 1st and 2nd instars submerged in the feeding medium exhibited extremely high rates of heartbeat, 7Hz at room temperature. These values are among the highest rates of heartbeat ever recorded in the animal kingdom. The fully grown larvae of the 3rd instar showed approximately half of the maximum heartbeat rate (3.5-4Hz), which became stabilized after pupariation to 2.5-2.7Hz.The larval heartbeat was always uni-directional, in the forward-oriented or anterograde direction and it was almost continuous. The slowly disintegrating, old larval heart used to beat at the constant frequency of 2.5-2.7Hz until complete cessation of all cardiac functions in 1-day-old puparium. In spite of the persisting constant heartbeat frequency, the transformation process of the larval heart was associated with successively decreasing amplitude of the systolic contractions and with the prolongation of the resting periods. The newly formed heart of the pupal-adult structure exhibited a qualitatively new pattern of heartbeat activity, which was manifested by periodic reversal of the heartbeat with the faster anterograde and slower retrograde phases. The frequencies of both of these reciprocal cardiac pulsations gradually increased during the advanced pharate adult period, reaching the values of 4-5Hz at the time of adult eclosion. Adult males and females also exhibited a perfect pattern of heartbeat reversal, with still very high rates of the anterograde heartbeat, in the range of 5-6Hz. In addition to the cardiac functions, we have recorded several kinds of extracardiac pulsations, which often interfered severely with the recordings of the heartbeat. There were strong, irregular extracardiac pulsations of a neurogenic nature (somatic muscles, oral armature) and relatively slow extracardiac pulsations of a myogenic nature (intestinal peristaltics, 0.2-0.3Hz). The extracardiac and cardiac pulsations were independent, their functions were not correlated. A possibility of creating new challenges in combination of molecular biology with the functional physiology of the heart have been discussed.     

An open real-time tele-stethoscopy system

ABSTRACT: BACKGROUND: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.     

A morphology alignment method for resampled heartbeat signals

A morphology alignment method for digital heartbeat signals of a person is proposed. In this method, a heartbeat is delineated into stable and flexible segments which are resampled at two different rates and then concatenated again. A resampling rate is decided based on the analysis of spatial error introduced by the interpolation method. Baseline shift of the resampled heartbeat is removed by amplitude normalization. The alignment of resampled heartbeat by this piecewise-uniform resampling method is then compared with those by uniform and non-uniform resampling methods. Four different morphological features are extracted from resampled heartbeats by each of three methods. Improvement of alignment is evaluated by to two metrics known as morphology alignment score and correlation coefficient. The method is tested on ECG signals obtained from two publicly available databases having different sampling frequencies. Statistical analyses suggest that the piecewise-uniform resampling method improves the alignment of morphology significantly. Computational time for the alignment is linear to the number of samples in a heartbeat and hence the method should be efficient enough for different practical applications using morphological features for automatic classification and decision making.     

Automatic classification of electrocardiogram signals based on transfer learning and continuous wavelet transform

Classification and subsequent diagnosis of cardiac arrhythmias is an important research topic in clinical practice. Confirmation of the type of arrhythmia at an early stage is critical for reducing the risk and occurrence of cardiovascular events. Nevertheless, diagnoses must be confirmed by a combination of specialist experience and electrocardiogram (ECG) examination, which can lead to delays in diagnosis. To overcome such obstacles, this study proposes an automatic ECG classification algorithm based on transfer learning and continuous wavelet transform (CWT). The transfer learning method is able to transfer the domain knowledge and features of images to a EGG, which is a one-dimensional signal when a convolutional neural network (CNN) is used for classification. Meanwhile, CWT is used to convert a one-dimensional ECG signal into a two-dimensional signal map consisting of time-frequency components. Considering that morphological features can be helpful in arrhythmia classification, eight features related to the R peak of an ECG signal are proposed. These auxiliary features are integrated with the features extracted by the CNN and then fed into the fully linked arrhythmia classification layer. The CNN developed in this study can also be used for bird activity detection. The classification experiments were performed after converting the two types of audio files containing songbird sounds and those without songbird sounds from the NIPS4Bplus bird song dataset into the Mel spectrum. Compared to the most recent methods in the same field, the classification results improved accuracy and recognition by 11.67% and 11.57%, respectively.     

Segmentation of heart sounds based on dynamic clustering

The heart sound signal is first separated into cycles, where the cycle detection is based on an instantaneous cycle frequency. The heart sound data of one cardiac cycle can be decomposed into a number of atoms characterized by timing delay, frequency, amplitude, time width and phase. To segment heart sounds, we made a hypothesis that the atoms of a heart sound congregate as a cluster in time–frequency domains. We propose an atom density function to indicate clusters. To suppress clusters of murmurs and noise, weighted density function by atom energy is further proposed to improve the segmentation of heart sounds. Therefore, heart sounds are indicated by the hybrid analysis of clustering and medical knowledge. The segmentation scheme is automatic and no reference signal is needed. Twenty-six subjects, including 3 normal and 23 abnormal subjects, were tested for heart sound signals in various clinical cases. Our statistics show that the segmentation was successful for signals collected from normal subjects and patients with moderate murmurs.     

Temperature-related heart rate in water and air and a comparison to other temperature-related measures of performance in the fiddler crab Leptuca pugilator (Bosc 1802)

Performance in poikilotherms is known to be sensitive to temperature, often with a low-sloping increase with temperature to a peak, and a steep decline with increasing temperature past the peak. We complemented past measures of performance by measuring heartbeat rates of the fiddler crab Leptuca pugilator in water and in air as a function of a range of temperatures previously shown to affect other measures of performance. In water over a range of 20–50 °C, heartbeat increased steadily to a peak at 40 °C and then steeply declined to near zero at 50 °C. In air, heartbeat also increased, but to a peak at 35 °C and then with a gentler decline than was found in water. Part of this different response may be due to evaporative water loss, which reduced body temperature in air, and therefore thermal stress, relative to body temperature when crabs were immersed in water. Increased availability of oxygen from air, according to the oxygen and capacity-limited thermal tolerance hypothesis, likely increased aerobic scope past the thermal peak, relative to within water, where oxygen delivery at higher temperatures may have been curtailed.We compared the heart rate performance relations to two previous measures of performance – endurance on a treadmill and sprint speed, both done in air. The peak performance temperature increased in the order: treadmill endurance time, sprint speed, heart rate in air, and heart rate in water, which demonstrates that different performance measures give different perspectives on the relation of thermal tolerance and fitness to temperature. Endurance may therefore be the limiting upper thermal stress factor in male fiddler crabs, when on hot sand flats. Temperature preference, found to be for temperatures <30 °C in air, could be a bet-hedging evolutionary strategy to avoid aerobic scope affecting endurance.     

Predicting species identity of bumblebees through analysis of flight buzzing sounds

We present a study of buzzing sounds of several common species of bumblebees, with the focus on automatic classification of bumblebee species and types. Such classification is useful for bumblebee monitoring, which is important in view of evaluating the quality of their living environment and protecting the biodiversity of these important pollinators. We analysed natural buzzing frequencies for queens and workers of 12 species. In addition, we analysed changes in buzzing of Bombus hypnorum worker for different types of behaviour. We developed a bumblebee classification application using machine learning algorithms. We extracted audio features from sound recordings using a large feature library. We used the best features to train a classification model, with Random Forest proving to be the best training algorithm on the testing set of samples. The web and mobile application also allows expert users to upload new recordings that can be later used to improve the classification model and expand it to include more species.