Extended segmented beat modulation method for cardiac beat classification and electrocardiogram denoising

none4noBeat classification and denoising are two challenging and fundamental operations when processing digital electrocardiograms (ECG). This paper proposes the extended segmented beat modulation method (ESBMM) as a tool for automatic beat classification and ECG denoising. ESBMM includes four main steps: (1) beat identification and segmentation into PQRS and TU segments; (2) wavelet-based time-frequency feature extraction; (3) convolutional neural network-based classification to discriminate among normal (N), supraventricular (S), and ventricular (V) beats; and (4) a template-based denoising procedure. ESBMM was tested using the MIT–BIH arrhythmia database available at Physionet. Overall, the classification accuracy was 91.5% while the positive predictive values were 92.8%, 95.6%, and 83.6%, for N, S, and V classes, respectively. The signal-to-noise ratio improvement after filtering was between 0.15 dB and 2.66 dB, with a median value equal to 0.99 dB, which is significantly higher than 0 (p < 0.05). Thus, ESBMM proved to be a reliable tool to classify cardiac beats into N, S, and V classes and to denoise ECG tracings.openNasim A.; Sbrollini A.; Morettini M.; Burattini L.Nasim, A.; Sbrollini, A.; Morettini, M.; Burattini, L

Low Power Circuits for Smart Flexible ECG Sensors

Cardiovascular diseases (CVDs) are the world leading cause of death. In-home heart condition monitoring effectively reduced the CVD patient hospitalization rate. Flexible electrocardiogram (ECG) sensor provides an affordable, convenient and comfortable in-home monitoring solution. The three critical building blocks of the ECG sensor i.e., analog frontend (AFE), QRS detector, and cardiac arrhythmia classifier (CAC), are studied in this research. A fully differential difference amplifier (FDDA) based AFE that employs DC-coupled input stage increases the input impedance and improves CMRR. A parasitic capacitor reuse technique is proposed to improve the noise/area efficiency and CMRR. An on-body DC bias scheme is introduced to deal with the input DC offset. Implemented in 0.35m CMOS process with an area of 0.405mm2, the proposed AFE consumes 0.9W at 1.8V and shows excellent noise effective factor of 2.55, and CMRR of 76dB. Experiment shows the proposed AFE not only picks up clean ECG signal with electrodes placed as close as 2cm under both resting and walking conditions, but also obtains the distinct -wave after eye blink from EEG recording. A personalized QRS detection algorithm is proposed to achieve an average positive prediction rate of 99.39% and sensitivity rate of 99.21%. The user-specific template avoids the complicate models and parameters used in existing algorithms while covers most situations for practical applications. The detection is based on the comparison of the correlation coefficient of the user-specific template with the ECG segment under detection. The proposed one-target clustering reduced the required loops. A continuous-in-time discrete-in-amplitude (CTDA) artificial neural network (ANN) based CAC is proposed for the smart ECG sensor. The proposed CAC achieves over 98% classification accuracy for 4 types of beats defined by AAMI (Association for the Advancement of Medical Instrumentation). The CTDA scheme significantly reduces the input sample numbers and simplifies the sample representation to one bit. Thus, the number of arithmetic operations and the ANN structure are greatly simplified. The proposed CAC is verified by FPGA and implemented in 0.18m CMOS process. Simulation results show it can operate at clock frequencies from 10KHz to 50MHz. Average power for the patient with 75bpm heart rate is 13.34W

Prediction of postoperative atrial fibrillation using the electrocardiogram: A proof of concept

Hospital patients recovering from major cardiac surgery are at high risk of postoperative atrial fibrillation (POAF), an arrhythmia which can be life-threatening. With the development of a tool to predict POAF early enough, the development of the arrhythmia could be potentially prevented using prophylactic treatments, thus reducing risks and hospital costs. To date, no reliable method suitable for autonomous clinical integration has been proposed yet. This thesis presents a study on the prediction of POAF using the electrocardiogram. A novel P-wave quality assessment tool to automatically identify high-quality P-waves was designed, and its clinical utility was assessed. Prediction of paroxysmal atrial fibrillation (AF) was performed by implementing and improving a selection of previously proposed methods. This allowed to perform a systematic comparison of those methods, and to test if their combination improved prediction of AF. Finally, prediction of POAF was tested in a clinically relevant scenario. This included studying the 48 hours preceding POAF, and automatically excluding noise-corrupted P-waves using the quality assessment tool. The P-wave quality assessment tool identified high-quality P-waves with high sensitivity (0.93) and good specificity (0.84). In addition, this tool improved the ability to predict AF, since it improved the precision of P-wave measurements. The best predictors of AF and POAF were measurements of the variability in P-wave time- and morphological features. Paroxysmal AF could be predicted with high specificity (0.93) and good sensitivity (0.82) when several predictors were combined. Furthermore, POAF could be predicted 48 hours before its onset with good sensitivity (0.74) and specificity (0.70). This leaves time for prophylactic treatments to be administered and possibly prevent POAF. Despite being promising, further work is required for these techniques to be useful in the clinical setting

Wavelet entropy as a measure of ventricular beat suppression from the electrocardiogram in atrial fibrillation

A novel method of quantifying the effectiveness of the suppression of ventricular activity from electrocardiograms (ECGs) in atrial fibrillation is proposed. The temporal distribution of the energy of wavelet coefficients is quantified by wavelet entropy at each ventricular beat. More effective ventricular activity suppression yields increased entropies at scales dominated by the ventricular and atrial components of the ECG. Two studies are undertaken to demonstrate the efficacy of the method: first, using synthesised ECGs with controlled levels of residual ventricular activity, and second, using patient recordings with ventricular activity suppressed by an average beat template subtraction algorithm. In both cases wavelet entropy is shown to be a good measure of the effectiveness of ventricular beat suppression

Flexible Time Series Matching for Clinical and Behavioral Data

Time Series data became broadly applied by the research community in the last decades after a massive explosion of its availability. Nonetheless, this rise required an improvement in the existing analysis techniques which, in the medical domain, would help specialists to evaluate their patients condition. One of the key tasks in time series analysis is pattern recognition (segmentation and classification). Traditional methods typically perform subsequence matching, making use of a pattern template and a similarity metric to search for similar sequences throughout time series. However, real-world data is noisy and variable (morphological distortions), making a template-based exact matching an elementary approach. Intending to increase flexibility and generalize the pattern searching tasks across domains, this dissertation proposes two Deep Learning-based frameworks to solve pattern segmentation and anomaly detection problems. Regarding pattern segmentation, a Convolution/Deconvolution Neural Network is proposed, learning to distinguish, point-by-point, desired sub-patterns from background content within a time series. The proposed framework was validated in two use-cases: electrocardiogram (ECG) and inertial sensor-based human activity (IMU) signals. It outperformed two conventional matching techniques, being capable of notably detecting the targeted cycles even in noise-corrupted or extremely distorted signals, without using any reference template nor hand-coded similarity scores. Concerning anomaly detection, the proposed unsupervised framework uses the reconstruction ability of Variational Autoencoders and a local similarity score to identify non-labeled abnormalities. The proposal was validated in two public ECG datasets (MITBIH Arrhythmia and ECG5000), performing cardiac arrhythmia identification. Results indicated competitiveness relative to recent techniques, achieving detection AUC scores of 98.84% (ECG5000) and 93.32% (MIT-BIH Arrhythmia).Dados de séries temporais tornaram-se largamente aplicados pela comunidade científica nas últimas decadas após um aumento massivo da sua disponibilidade. Contudo, este aumento exigiu uma melhoria das atuais técnicas de análise que, no domínio clínico, auxiliaria os especialistas na avaliação da condição dos seus pacientes. Um dos principais tipos de análise em séries temporais é o reconhecimento de padrões (segmentação e classificação). Métodos tradicionais assentam, tipicamente, em técnicas de correspondência em subsequências, fazendo uso de um padrão de referência e uma métrica de similaridade para procurar por subsequências similares ao longo de séries temporais. Todavia, dados do mundo real são ruidosos e variáveis (morfologicamente), tornando uma correspondência exata baseada num padrão de referência uma abordagem rudimentar. Pretendendo aumentar a flexibilidade da análise de séries temporais e generalizar tarefas de procura de padrões entre domínios, esta dissertação propõe duas abordagens baseadas em Deep Learning para solucionar problemas de segmentação de padrões e deteção de anomalias. Acerca da segmentação de padrões, a rede neuronal de Convolução/Deconvolução proposta aprende a distinguir, ponto a ponto, sub-padrões pretendidos de conteúdo de fundo numa série temporal. O modelo proposto foi validado em dois casos de uso: sinais eletrocardiográficos (ECG) e de sensores inerciais em atividade humana (IMU). Este superou duas técnicas convencionais, sendo capaz de detetar os ciclos-alvo notavelmente, mesmo em sinais corrompidos por ruído ou extremamente distorcidos, sem o uso de nenhum padrão de referência nem métricas de similaridade codificadas manualmente. A respeito da deteção de anomalias, a técnica não supervisionada proposta usa a capacidade de reconstrução dos Variational Autoencoders e uma métrica de similaridade local para identificar anomalias desconhecidas. A proposta foi validada na identificação de arritmias cardíacas em duas bases de dados públicas de ECG (MIT-BIH Arrhythmia e ECG5000). Os resultados revelam competitividade face a técnicas recentes, alcançando métricas AUC de deteção de 93.32% (MIT-BIH Arrhythmia) e 98.84% (ECG5000)

Extracting heart rate dependent electrocardiogram templates for a body emulator environment

Abstract. Medical device and analysis method developments often include tests on humans, which are expensive, time consuming, and sometimes even dangerous. In order to perform human tests, special safety conditions and ethical and legal requirements must be taken into account. Emulators that can emulate the physiological functions of the human body could solve these difficulties. In this study, the heart rate depended electrocardiogram templates for this kind of an emulator were extracted. The real-life electrocardiogram preprocessing included a high-pass filter and a Savitzky-Golay filter. A beat detection algorithm was developed to detect QRS complexes in the signals and classify beat artefacts based on the RR interval sequences and two adaptive thresholds. Heart rate levels were detected using the K-means clustering technique. Vectorcardiogram signals were converted from the electrocardiogram signals using the inverse Dower’s transformation matrix, and vectorcardiogram templates were extracted to the respective heart rate levels. Finally, a graphical user interface was created for the mentioned methods. The developed beat detection algorithm was tested with the MIT-BIH Arrhythmia Database and the comparison was made with the state-of-the-art algorithms. The beat detection algorithm resulted the sensitivity of 99.77 \%, precision of 99.65 \%, and detection error rate of 0.58 \%. Based on the results, the proposed methods and extracted vectorcardiogram templates were successful.Sykkeestä riippuvien elektrokardiogrammimallien poiminta kehoemulaattoriympäristöön. Tiivistelmä. Lääketieteellisten laitteiden ja analyysimenetelmien kehitystyö sisältää usein ihmisille suoritettavia testejä, jotka ovat kalliita, aikaa vieviä ja joskus jopa vaarallisia. Ihmiskokeiden toteuttamiseksi on otettava huomioon erityisiä turvallisuusehtoja, sekä eettisiä ja laillisia vaatimuksia. Emulaattorit, jotka pystyvät jäljittelemään ihmiskehon fysiologisia toimintoja, voivat olla ratkaisu näihin ongelmiin. Tässä tutkimuksessa sykkeestä riippuvia elektrokardiogrammimalleja poimittiin tämän tyyppiselle emulaattorille. Tosielämän elektrokardiogrammisignaalien esikäsittely sisälsi ylipäästösuodattimen ja Savitzky-Golay suodattimen. Sydämen lyöntien tunnistussalgoritmi kehitettiin tunnistamaan QRS-komplekseja signaaleista ja luokittelemaan lyöntiartefakteja RR-intervallisekvenssien ja kahden adaptiivisen kynnysarvon perusteella. Syketasot tunnistettiin käyttämällä K-means klusterointitekniikkaa. Vektorikardiogrammisignaalit muunnettiin elektrokardiogrammisignaaleista käyttämällä käänteistä Dowerin muunnosmatriisia ja vektorikardiogrammimallit poimittiin vastaaville syketasoille. Lopuksi luotiin graafnen käyttöliittymä mainituille menetelmille. Kehitetty lyöntien tunnistusalgoritmi testattiin MIT-BIH Arrhythmia Database-tietokannalla ja vertailu suoritettiin vastaavien algoritmien kanssa. Algoritmi suoriutui 99,77 % herkkyydellä, 99,65 % spesifsyydellä ja 0,58 % virheprosentilla. Tulosten perusteella ehdotetut menetelmät ja poimitut vektorikardiogrammimallit olivat onnistuneita

A method for context-based adaptive QRS clustering in real-time

Continuous follow-up of heart condition through long-term electrocardiogram monitoring is an invaluable tool for diagnosing some cardiac arrhythmias. In such context, providing tools for fast locating alterations of normal conduction patterns is mandatory and still remains an open issue. This work presents a real-time method for adaptive clustering QRS complexes from multilead ECG signals that provides the set of QRS morphologies that appear during an ECG recording. The method processes the QRS complexes sequentially, grouping them into a dynamic set of clusters based on the information content of the temporal context. The clusters are represented by templates which evolve over time and adapt to the QRS morphology changes. Rules to create, merge and remove clusters are defined along with techniques for noise detection in order to avoid their proliferation. To cope with beat misalignment, Derivative Dynamic Time Warping is used. The proposed method has been validated against the MIT-BIH Arrhythmia Database and the AHA ECG Database showing a global purity of 98.56% and 99.56%, respectively. Results show that our proposal not only provides better results than previous offline solutions but also fulfills real-time requirements.Comment: 12 pages, 6 figure