An algorithm for heart rate extraction from acoustic recordings at the neck

Heart rate is an important physiological parameter to assess the cardiac condition of an individual and is traditionally determined by attaching multiple electrodes on the chest of a subject to record the electrical activity of the heart. The installation and handling complexities of such systems does not prove feasible for a user to undergo a long-term monitoring in the home settings. A small-sized, battery-operated wearable monitoring device is placed on the suprasternal notch at neck to record acoustic signals containing information about breathing and cardiac sounds. The heart sounds obtained are heavily corrupted by the respiratory cycles and other external artifacts. This paper presents a novel algorithm for reliably extracting the heart rate from such acoustic recordings, keeping in mind the constraints posed by the wearable technology. The methodology constructs the Hilbert energy envelope of the signal by calculating its instantaneous characteristics to segment and classify a cardiac cycle into S1 and S2 sounds using their timing characteristics. The algorithm is tested on a dataset consisting of 13 subjects with an approximate data length of 75 hours and achieves an accuracy of 94.34%, an RMS error of 3.96 bpm and a correlation coefficient of 0.93 with reference to a commercial device in use

Deep Learning in Cardiology

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table

Segmentation and Parametrization of the Phonocardiogram for the Heart Conditions Classification in Newborns

Phonocardiographs are analyzed for diagnostics of heart conditions in newborns. The algorithms of allocation of heart tones and selection of stationary periods on phonocardiograms are proposed. Dedicated heartbeats can be parameterized in different ways. The first set of parameters characterizes the shape and the time-amplitude features. The second set of parameters is the coefficients of the frequency-time decomposition of cardiac cycles with spline bases. This approach allows detecting of the Patent ductus arteriosus (PDA) by machine learning methods. Software for phonograms analysis has been developed

Automatic analysis and classification of cardiac acoustic signals for long term monitoring

Objective: Cardiovascular diseases are the leading cause of death worldwide resulting in over 17.9 million deaths each year. Most of these diseases are preventable and treatable, but their progression and outcomes are significantly more positive with early-stage diagnosis and proper disease management. Among the approaches available to assist with the task of early-stage diagnosis and management of cardiac conditions, automatic analysis of auscultatory recordings is one of the most promising ones, since it could be particularly suitable for ambulatory/wearable monitoring. Thus, proper investigation of abnormalities present in cardiac acoustic signals can provide vital clinical information to assist long term monitoring. Cardiac acoustic signals, however, are very susceptible to noise and artifacts, and their characteristics vary largely with the recording conditions which makes the analysis challenging. Additionally, there are challenges in the steps used for automatic analysis and classification of cardiac acoustic signals. Broadly, these steps are the segmentation, feature extraction and subsequent classification of recorded signals using selected features. This thesis presents approaches using novel features with the aim to assist the automatic early-stage detection of cardiovascular diseases with improved performance, using cardiac acoustic signals collected in real-world conditions. Methods: Cardiac auscultatory recordings were studied to identify potential features to help in the classification of recordings from subjects with and without cardiac diseases. The diseases considered in this study for the identification of the symptoms and characteristics are the valvular heart diseases due to stenosis and regurgitation, atrial fibrillation, and splitting of fundamental heart sounds leading to additional lub/dub sounds in the systole or diastole interval of a cardiac cycle. The localisation of cardiac sounds of interest was performed using an adaptive wavelet-based filtering in combination with the Shannon energy envelope and prior information of fundamental heart sounds. This is a prerequisite step for the feature extraction and subsequent classification of recordings, leading to a more precise diagnosis. Localised segments of S1 and S2 sounds, and artifacts, were used to extract a set of perceptual and statistical features using wavelet transform, homomorphic filtering, Hilbert transform and mel-scale filtering, which were then fed to train an ensemble classifier to interpret S1 and S2 sounds. Once sound peaks of interest were identified, features extracted from these peaks, together with the features used for the identification of S1 and S2 sounds, were used to develop an algorithm to classify recorded signals. Overall, 99 features were extracted and statistically analysed using neighborhood component analysis (NCA) to identify the features which showed the greatest ability in classifying recordings. Selected features were then fed to train an ensemble classifier to classify abnormal recordings, and hyperparameters were optimized to evaluate the performance of the trained classifier. Thus, a machine learning-based approach for the automatic identification and classification of S1 and S2, and normal and abnormal recordings, in real-world noisy recordings using a novel feature set is presented. The validity of the proposed algorithm was tested using acoustic signals recorded in real-world, non-controlled environments at four auscultation sites (aortic valve, tricuspid valve, mitral valve, and pulmonary valve), from the subjects with and without cardiac diseases; together with recordings from the three large public databases. The performance metrics of the methodology in relation to classification accuracy (CA), sensitivity (SE), precision (P+), and F1 score, were evaluated. Results: This thesis proposes four different algorithms to automatically classify fundamental heart sounds – S1 and S2; normal fundamental sounds and abnormal additional lub/dub sounds recordings; normal and abnormal recordings; and recordings with heart valve disorders, namely the mitral stenosis (MS), mitral regurgitation (MR), mitral valve prolapse (MVP), aortic stenosis (AS) and murmurs, using cardiac acoustic signals. The results obtained from these algorithms were as follows: • The algorithm to classify S1 and S2 sounds achieved an average SE of 91.59% and 89.78%, and F1 score of 90.65% and 89.42%, in classifying S1 and S2, respectively. 87 features were extracted and statistically studied to identify the top 14 features which showed the best capabilities in classifying S1 and S2, and artifacts. The analysis showed that the most relevant features were those extracted using Maximum Overlap Discrete Wavelet Transform (MODWT) and Hilbert transform. • The algorithm to classify normal fundamental heart sounds and abnormal additional lub/dub sounds in the systole or diastole intervals of a cardiac cycle, achieved an average SE of 89.15%, P+ of 89.71%, F1 of 89.41%, and CA of 95.11% using the test dataset from the PASCAL database. The top 10 features that achieved the highest weights in classifying these recordings were also identified. • Normal and abnormal classification of recordings using the proposed algorithm achieved a mean CA of 94.172%, and SE of 92.38%, in classifying recordings from the different databases. Among the top 10 acoustic features identified, the deterministic energy of the sound peaks of interest and the instantaneous frequency extracted using the Hilbert Huang-transform, achieved the highest weights. • The machine learning-based approach proposed to classify recordings of heart valve disorders (AS, MS, MR, and MVP) achieved an average CA of 98.26% and SE of 95.83%. 99 acoustic features were extracted and their abilities to differentiate these abnormalities were examined using weights obtained from the neighborhood component analysis (NCA). The top 10 features which showed the greatest abilities in classifying these abnormalities using recordings from the different databases were also identified. The achieved results demonstrate the ability of the algorithms to automatically identify and classify cardiac sounds. This work provides the basis for measurements of many useful clinical attributes of cardiac acoustic signals and can potentially help in monitoring the overall cardiac health for longer duration. The work presented in this thesis is the first-of-its-kind to validate the results using both, normal and pathological cardiac acoustic signals, recorded for a long continuous duration of 5 minutes at four different auscultation sites in non-controlled real-world conditions.Open Acces

Characterization, Classification, and Genesis of Seismocardiographic Signals

Seismocardiographic (SCG) signals are the acoustic and vibration induced by cardiac activity measured non-invasively at the chest surface. These signals may offer a method for diagnosing and monitoring heart function. Successful classification of SCG signals in health and disease depends on accurate signal characterization and feature extraction. In this study, SCG signal features were extracted in the time, frequency, and time-frequency domains. Different methods for estimating time-frequency features of SCG were investigated. Results suggested that the polynomial chirplet transform outperformed wavelet and short time Fourier transforms. Many factors may contribute to increasing intrasubject SCG variability including subject posture and respiratory phase. In this study, the effect of respiration on SCG signal variability was investigated. Results suggested that SCG waveforms can vary with lung volume, respiratory flow direction, or a combination of these criteria. SCG events were classified into groups belonging to these different respiration phases using classifiers, including artificial neural networks, support vector machines, and random forest. Categorizing SCG events into different groups containing similar events allows more accurate estimation of SCG features. SCG feature points were also identified from simultaneous measurements of SCG and other well-known physiologic signals including electrocardiography, phonocardiography, and echocardiography. Future work may use this information to get more insights into the genesis of SCG

Analyse et reconnaissance de signaux vibratoires : contribution au traitement et à l'analyse de signaux cardiaques pour la télémédecine

The heart is a muscle. Its mechanical operation is like a pump charged for distributing and retrieving the blood in the lungs and cardiovascular system. Its electrical operation is regulated by the sinus node, a pacemaker or electric regulator responsible for triggering the natural heart beats that punctuate the functioning of the body.Doctors monitor the electromechanical functioning of the heart by recording an electrical signal called an electrocardiogram (ECG) or an audible signal : the phonocardiogram (PCG). The analysis and processing of these two signals are essential for diagnosis, to help detect anomalies and cardiac pathologies.The objective of this thesis is to develop signal processing tools on ECG and PCG to assist cardiologist in his analysis of these signals. The basic idea is to develop algorithms of low complexity and having inexpensive computing time. The primary interest is to ensure their easy implementation in a mobile heart monitoring system for use by the doctor or the patient. The second advantage lies in the possibility of automatic real-time analysis of signals with the mobile device, allowing control of the transmission of these signals to a removal of doubt.Numerous studies have led to significant advances in the analysis of ECG signals and the automatic recognition of cardiac conditions. Databases of real or synthetic signals annotated also assess the performance of new methods. PCG signals are much less studied, difficult to analyze and to interpret. The main methods (Fourier, wavelet and Wigner Ville) were tested : they do not allow automatic recognition of signatures, and an accurate understanding of their contents.Wavelet Transform (WT) on cardiac signals showed its effectiveness to filter and locate useful information, but it involves an external processing function (mother wavelet) whose the choice depends on the prior knowledge on the signal to be processed. This is not always suitable for cardiac signals. Moreover, the wavelet transform generally induces inaccuracies in the location due to the external function and optionally due to the sub- sampling of the signatures.The non-stationary nature of the ECG and PCG and their sensitivity to noise makes it difficult to separate an informative transition of a transition due to measurement noise. The choice of treatment tool should allow denoising and analysis of these signals without alteration or the processing tool delocalization of the singularities.In response to our objectives and considering these problems, we propose to rely primarily on empirical mode decomposition (EMD) and Hilbert Huang Transform (HHT) to develop solutions. The EMD is a non linear approach decomposing the signal in intrinsic signal (IMF), oscillations of the type FM-AM, giving a time/scale signal representation. Associated with the Hilbert transform (TH), the THH determines the instantaneous amplitude (IA) and instantaneous frequency (IF) of each mode, leading to a time/frequency representation of the ECG and PCG.Without involving an external function, EMD approach can restore (noise reduction), analyze and reconstruct the signal without relocation of its singularities. This approach allows to locate R peaks of the ECG, heart rate and study the cardiac frequency variability (CFV), locate and analyze the sound components B1 and B2 of the PCG.Among the trials and developments that we made, we present in particular a new method (EDA : empirical denoising approach) inspired by the EMD approach for denoising cardiac signals. We also set out the implementation of two approaches for locating ECG signature (QRS complex, T and P waves). The first is based on the detection of local maxima : in using Modulus Maxima and Lipschitz exponent followed by a classifier. The second uses NFLS, wich an nonlinear approach for the detection and location of unique transitions in the discrete domain.Le coeur est un muscle. Son fonctionnement mécanique est celui d'une pompe chargée de distribuer et de récupérer le sang dans les poumons et dans le système cardiovasculaire. Son fonctionnement électrique est régulé par le son noeud sinusal, un stimulateur ou régulateur électrique chargé de déclencher les battements naturels du coeur qui rythment le fonctionnement du corps. Les médecins surveillent ce fonctionnement électromécanique du coeur en enregistrant un signal électrique appelé électrocardiogramme (ECG) ou un signal sonore : le phono-cardiogramme (PCG). L'analyse et le traitement de ces deux signaux sont fondamentaux pour établir un diagnostic et aider à déceler des anomalies et des pathologies cardiaques. L’objectif de cette thèse est de développer des techniques de traitement des signaux ECG et notamment PCG afin d’aider le médecin dans son analyse de ces signaux. L’idée de fond est de mettre en point des algorithmes relativement simples et peu coûteux en temps de calcul. Le premier intérêt serait de garantir leur implantation aisée dans un système mobile de surveillance cardiaque à l’usage du médecin, voire du patient. Le deuxième intérêt réside dans la possibilité d’une analyse automatique en temps réel des signaux avec le dispositif mobile, autorisant le choix de la transmission de ces signaux pour une levée de doute. De nombreux travaux ont mené à des avancées significatives dans l’analyse des signaux ECG et la reconnaissance automatiques des pathologies cardiaques. Des bases de données de signaux réels ou synthétiques annotées permettent également d’évaluer les performances de toute nouvelle méthode. Quant aux signaux PCG, ils sont nettement moins étudiés, difficiles à analyser et à interpréter. Même si les grandes familles de méthodes (Fourier, Wigner Ville et ondelettes) ont été testées, elles n’autorisent pas une reconnaissance automatique des signatures, d’en avoir une analyse et une compréhension assez fines.La Transformée en Ondelettes (TO) sur les signaux cardiaques a montré son efficacité pour filtrer et localiser les informations utiles mais elle fait intervenir une fonction externe de traitement (ondelette mère) dont le choix dépend de la connaissance au préalable du signal à traiter. Ce n'est pas toujours adapté aux signaux cardiaques. De plus, la Transformée en ondelettes induit généralement une imprécision dans la localisation due à la fonction externe et éventuellement au sous-échantillonnage des signatures. La nature non stationnaire de l'ECG et du PCG et leur sensibilité aux bruits rendent difficile la séparation d’une transition informative d'une transition due aux bruits de mesure. Le choix de l'outil de traitement doit permettre un débruitage et une analyse de ces signaux sans délocalisation des singularités ni altération de leurs caractéristiques. En réponse à nos objectifs et considérant ces différents problèmes, nous proposons de nous appuyer principalement sur la décomposition modale empirique (EMD) ou transformée de Hilbert Huang (THH) pour développer des solutions. L’EMD est une approche non linéaire capable de décomposer le signal étudié en fonctions modales intrinsèques (IMF), oscillations du type FM-AM, donnant ainsi une représentation temps/échelle du signal. Associée à la transformée de Hilbert (TH), la THH permet de déterminer les amplitudes instantanées (AI) et les fréquences instantanées (FI) de chaque mode, menant ainsi à une représentation temps/fréquence des signaux.Sans faire intervenir une fonction externe, on peut ainsi restaurer (réduction de bruit), analyser et reconstruire le signal sans délocalisation de ses singularités. Cette approche permet de localiser les pics R de l'ECG, déterminer le rythme cardiaque et étudier la variabilité fréquentielle cardiaque (VFC), localiser et analyser les composantes des bruits B1 et B2 du PCG

Multidimensional embedded MEMS motion detectors for wearable mechanocardiography and 4D medical imaging

Background: Cardiovascular diseases are the number one cause of death. Of these deaths, almost 80% are due to coronary artery disease (CAD) and cerebrovascular disease. Multidimensional microelectromechanical systems (MEMS) sensors allow measuring the mechanical movement of the heart muscle offering an entirely new and innovative solution to evaluate cardiac rhythm and function. Recent advances in miniaturized motion sensors present an exciting opportunity to study novel device-driven and functional motion detection systems in the areas of both cardiac monitoring and biomedical imaging, for example, in computed tomography (CT) and positron emission tomography (PET). Methods: This Ph.D. work describes a new cardiac motion detection paradigm and measurement technology based on multimodal measuring tools — by tracking the heart’s kinetic activity using micro-sized MEMS sensors — and novel computational approaches — by deploying signal processing and machine learning techniques—for detecting cardiac pathological disorders. In particular, this study focuses on the capability of joint gyrocardiography (GCG) and seismocardiography (SCG) techniques that constitute the mechanocardiography (MCG) concept representing the mechanical characteristics of the cardiac precordial surface vibrations. Results: Experimental analyses showed that integrating multisource sensory data resulted in precise estimation of heart rate with an accuracy of 99% (healthy, n=29), detection of heart arrhythmia (n=435) with an accuracy of 95-97%, ischemic disease indication with approximately 75% accuracy (n=22), as well as significantly improved quality of four-dimensional (4D) cardiac PET images by eliminating motion related inaccuracies using MEMS dual gating approach. Tissue Doppler imaging (TDI) analysis of GCG (healthy, n=9) showed promising results for measuring the cardiac timing intervals and myocardial deformation changes. Conclusion: The findings of this study demonstrate clinical potential of MEMS motion sensors in cardiology that may facilitate in time diagnosis of cardiac abnormalities. Multidimensional MCG can effectively contribute to detecting atrial fibrillation (AFib), myocardial infarction (MI), and CAD. Additionally, MEMS motion sensing improves the reliability and quality of cardiac PET imaging.Moniulotteisten sulautettujen MEMS-liiketunnistimien käyttö sydänkardiografiassa sekä lääketieteellisessä 4D-kuvantamisessa Tausta: Sydän- ja verisuonitaudit ovat yleisin kuolinsyy. Näistä kuolemantapauksista lähes 80% johtuu sepelvaltimotaudista (CAD) ja aivoverenkierron häiriöistä. Moniulotteiset mikroelektromekaaniset järjestelmät (MEMS) mahdollistavat sydänlihaksen mekaanisen liikkeen mittaamisen, mikä puolestaan tarjoaa täysin uudenlaisen ja innovatiivisen ratkaisun sydämen rytmin ja toiminnan arvioimiseksi. Viimeaikaiset teknologiset edistysaskeleet mahdollistavat uusien pienikokoisten liiketunnistusjärjestelmien käyttämisen sydämen toiminnan tutkimuksessa sekä lääketieteellisen kuvantamisen, kuten esimerkiksi tietokonetomografian (CT) ja positroniemissiotomografian (PET), tarkkuuden parantamisessa. Menetelmät: Tämä väitöskirjatyö esittelee uuden sydämen kineettisen toiminnan mittaustekniikan, joka pohjautuu MEMS-anturien käyttöön. Uudet laskennalliset lähestymistavat, jotka perustuvat signaalinkäsittelyyn ja koneoppimiseen, mahdollistavat sydämen patologisten häiriöiden havaitsemisen MEMS-antureista saatavista signaaleista. Tässä tutkimuksessa keskitytään erityisesti mekanokardiografiaan (MCG), joihin kuuluvat gyrokardiografia (GCG) ja seismokardiografia (SCG). Näiden tekniikoiden avulla voidaan mitata kardiorespiratorisen järjestelmän mekaanisia ominaisuuksia. Tulokset: Kokeelliset analyysit osoittivat, että integroimalla usean sensorin dataa voidaan mitata syketiheyttä 99% (terveillä n=29) tarkkuudella, havaita sydämen rytmihäiriöt (n=435) 95-97%, tarkkuudella, sekä havaita iskeeminen sairaus noin 75% tarkkuudella (n=22). Lisäksi MEMS-kaksoistahdistuksen avulla voidaan parantaa sydämen 4D PET-kuvan laatua, kun liikeepätarkkuudet voidaan eliminoida paremmin. Doppler-kuvantamisessa (TDI, Tissue Doppler Imaging) GCG-analyysi (terveillä, n=9) osoitti lupaavia tuloksia sydänsykkeen ajoituksen ja intervallien sekä sydänlihasmuutosten mittaamisessa. Päätelmä: Tämän tutkimuksen tulokset osoittavat, että kardiologisilla MEMS-liikeantureilla on kliinistä potentiaalia sydämen toiminnallisten poikkeavuuksien diagnostisoinnissa. Moniuloitteinen MCG voi edistää eteisvärinän (AFib), sydäninfarktin (MI) ja CAD:n havaitsemista. Lisäksi MEMS-liiketunnistus parantaa sydämen PET-kuvantamisen luotettavuutta ja laatua