Beat Segmentation Research Articles

Real-Time Multilead Convolutional Neural Network for Myocardial Infarction Detection.

In this paper, a novel algorithm based on a convolutional neural network (CNN) is proposed for myocardial infarction detection via multilead electrocardiogram (ECG). A beat segmentation algorithm utilizing multilead ECG is designed to obtain multilead beats, and fuzzy information granulation is adopted for preprocessing. Then, the beats are input into our multilead-CNN (ML-CNN), a novel model that includes sub two-dimensional (2-D) convolutional layers and lead asymmetric pooling (LAP) layers. As different leads represent various angles of the same heart, LAP can capture multiscale features of different leads, exploiting the individual characteristics of each lead. In addition, sub 2-D convolution can utilize the holistic characters of all the leads. It uses 1-D kernels shared among the different leads to generate local optimal features. These strategies make the ML-CNN suitable for multilead ECG processing. To evaluate our algorithm, actual ECG datasets from the PTB diagnostic database are used. The sensitivity of our algorithm is 95.40%, the specificity is 97.37%, and the accuracy is 96.00% in the experiments. Targeting lightweight mobile healthcare applications, real-time analyses are performed on both MATLAB and ARM Cortex-A9 platforms. The average processing times for each heartbeat are approximately 17.10 and 26.75 ms, respectively, which indicate that this method has good potential for mobile healthcare applications.

188 Development of a novel software package for high-throughput processing and analysis of cardiac optical mapping data

BackgroundOptical mapping is a powerful research tool that is revolutionising study of cardiac electrophysiology. However, processing and analysis of optical mapping data is computationally challenging to design and implement, a...

Heart sound segmentation using fractal decomposition.

In order to assist cardiac diagnosis by phonocardiography, the automated identification of fundamental heart sounds for heart beat segmentation in a cardiac cycle plays a significant role in signal processing. Recent advancements in signal processing have also shown the potential of multifractality in biomedical applications. Hence, in this paper, the multifractal property of heart sounds is utilized to identify first and second heart sounds. The root mean square (rms) fluctuation used to obtain multifractal/singularity spectrum is used to decompose the heart sound into its own fractally-important components in time domain along with simultaneous Gaussianity test to filter out fundamental components. The performance is evaluated on an experimental database of 23 different heart sounds and 6 patients' recordings done in a real clinical environment. Simulation results have shown that it is a promising approach in Heart Sound Segmentation (HSS).

Electrocardiogram signal compression using singular coefficient truncation and wavelet coefficient coding

In this study, an inter- and intra-beat correlation-based compression technique for electrocardiogram (ECG) signal is proposed using singular coefficient truncation, based on singular value decomposition (SVD) and adaptive scanning wavelet difference reduction technique. In this method, correlated beat segments are arranged in N × M array, and processed with proposed compression technique. Initially, array is decomposed into triplets using SVD in which most significant energy is concentrated in first few singular values. The insignificant energy coefficients are truncated and array is retained with few significant singular coefficients. Retained array is further processed with ASDWR coding technique to achieve higher compression. The proposed technique is tested on several MIT-BIH arrhythmia ECG signal records, and performance is evaluated in form of compression ratio (CR), percentage root-mean square difference (PRD), signal-to-noise ratio and cross-correlation. The evaluation results illustrate that the proposed algorithm has achieved the CR of 24.85:1 with an excellent quality of signal reconstruction in terms of PRD as 1.89% for ECG signal Rec. 100.

A robust QRS detection using novel pre-processing techniques and kurtosis based enhanced efficiency

The QRS complex detection is very imperative step prior to detecting any other wave component, beat segments, or any other morphological parameters of electrocardiogram (ECG). The paper presents a novel technique for QRS complex detection with minimum pre-processing required. The technique uses two-stage median filter and Savitzky–Golay smoothing filter for pre-processing. The decision rule is based on root-mean-square of the signal. The technique evolves with a novel approach for isoelectric line detection and a kurtosis-based false positive correction method. The robustness of the proposed technique been substantiated on Fantasia Database (FTD), MIT-BIH Arrhythmia Database (MIT-AD), MIT-BIH Normal Sinus Rhythm Database (MIT-NSD), and BIDMC Congestive Heart Failure Database (CHFD). The detection sensitivity (Se), positive predictivity (+P), detection error rate (DER), and accuracy (Ac) of proposed technique on FTD is Se=99.90%, +P=99.91%, DER=0.19%, and Ac=99.81%. On MIT-AD the technique has Se=99.50%, +P=99.56%, DER=0.93%, and Ac=99.08%. The technique shows Se=99.36%, +P=99.43%, DER=1.21%, and Ac=98.81% on MIT-NSD. Tested on CHFD, the techniques has Se=99.68%, +P=99.78%, DER=0.52%, and Ac=99.46%. The technique is also tested on self-recorded data-set and has Se=99.97%, +P=99.99%, DER=0.04%, and Ac=99.96%. The observation of above results shows the high performance and robustness of QRS complex detection, which will lead to highly accurate cardiac health prognosis.

Hybrid method based on singular value decomposition and embedded zero tree wavelet technique for ECG signal compression

Background and objectiveIn the field of biomedical, it becomes necessary to reduce data quantity due to the limitation of storage in real-time ambulatory system and telemedicine system. Research has been underway since very beginning for the development of an efficient and simple technique for longer term benefits. MethodThis paper, presents an algorithm based on singular value decomposition (SVD), and embedded zero tree wavelet (EZW) techniques for ECG signal compression which deals with the huge data of ambulatory system. The proposed method utilizes the low rank matrix for initial compression on two dimensional (2-D) ECG data array using SVD, and then EZW is initiated for final compression. Initially, 2-D array construction has key issue for the proposed technique in pre-processing. Here, three different beat segmentation approaches have been exploited for 2-D array construction using segmented beat alignment with exploitation of beat correlation. The proposed algorithm has been tested on MIT-BIH arrhythmia record, and it was found that it is very efficient in compression of different types of ECG signal with lower signal distortion based on different fidelity assessments. ResultsThe evaluation results illustrate that the proposed algorithm has achieved the compression ratio of 24.25:1 with excellent quality of signal reconstruction in terms of percentage-root-mean square difference (PRD) as 1.89% for ECG signal Rec. 100 and consumes only 162bps data instead of 3960bps uncompressed data. ConclusionThe proposed method is efficient and flexible with different types of ECG signal for compression, and controls quality of reconstruction. Simulated results are clearly illustrate the proposed method can play a big role to save the memory space of health data centres as well as save the bandwidth in telemedicine based healthcare systems.

QRS detection algorithm based on the quadratic filter

QRS detection in the electrocardiogram signal is very crucial as a preliminary step for obtaining QRS complex, beat segmentation, and beat-to-beat intervals. Two main problems in QRS detection are a variety of noise types and various types of abnormal morphologies. We propose a QRS detection algorithm consisting of the quadratic filter for enhancing QRS signal to noise ratio. Results show that significant improvement in QRS signal to noise ratio can be obtained from challenging situations including low amplitude QRS complexes corrupted by baseline drift and abnormal morphologies such as an aberrated atrial premature beat, a premature ventricular contraction beat, a fusion of ventricular and normal beat, and a fusion of paced and normal beat. The enhancements in QRS signal to noise ratio allow us to use a single fixed threshold without any additional post-processing techniques in beat detection step. The performance of proposed algorithm was evaluated with the electrocardiogram data from MIT-BIH arrhythmia database. Results show that the quadratic filter is capable of enhancing QRS signal to noise very well leading to the average detection error rate of 0.38% from 48 records.

Music content authentication based on beat segmentation and fuzzy classification

Digital audio has been ubiquitous over the past decade. Since it can be easily modified by editing tools, there has been a strong need to protect its content for secure multimedia applications. Previous audio authentication algorithms are mainly focused on either human speech or general audio with music as part of the test data, while special research on music authentication has been somewhat neglected. In this article, we propose a novel algorithm to protect the integrity and authenticity of music signals. Its main contributions include the following: (1) Music is segmented into beat-based frames, which not only endows the authentication units with more semantic meaning but also perfectly resolves the challenging synchronization problem. (2) Robust hashes are generated from chroma-based mid-level audio feature which can appropriately characterize the music content and integrated with an encryption procedure to ensure the security against malicious block-wise vector quantization attack. (3) Fuzzy logic is adopted to make the authentication decision in the light of three measures defined on bit errors, coinciding with the inherent blurred nature of authentication. The experiments exhibit good discriminative ability between admissible and malicious operations.

Time-Varying Coherence Function for Atrial Fibrillation Detection

We introduce a novel method for the automatic detection of atrial fibrillation (AF) using time-varying coherence functions (TVCF). The TVCF is estimated by the multiplication of two time-varying transfer functions (TVTFs). The two TVTFs are obtained using two adjacent data segments with one data segment as the input signal and the other data segment as the output to produce the first TVTF; the second TVTF is produced by reversing the input and output signals. We found that the resultant TVCF between two adjacent normal sinus rhythm (NSR) segments shows high coherence values (near 1) throughout the entire frequency range. However, if either or both segments partially or fully contain AF, the resultant TVCF is significantly lower than 1. When TVCF was combined with Shannon entropy (SE), we obtained even more accurate AF detection rate of 97.9% for the MIT-BIH atrial fibrillation (AF) database (n = 23) with 128 beat segments. The detection algorithm was tested on four databases using 128 beat segments: the MIT-BIH AF database, the MIT-BIH NSR database ( n = 18), the MIT-BIH Arrhythmia database ( n = 48), and a clinical 24-h Holter AF database ( n = 25). Using the receiver operating characteristic curves from the combination of TVCF and SE, we obtained a sensitivity of 98.2% and specificity of 97.7% for the MIT-BIH AF database. For the MIT-BIH NSR database, we found a specificity of 99.7%. For the MIT-BIH Arrhythmia database, the sensitivity and specificity were 91.1% and 89.7%, respectively. For the clinical database (24-h Holter data), the sensitivity and specificity were 92.3% and 93.6%, respectively. We also found that a short segment (12 beats) also provided accurate AF detection for all databases: sensitivity of 94.7% and specificity of 90.4% for the MIT-BIH AF, specificity of 94.4% for the MIT-BIH-NSR, the sensitivity of 92.4% and specificity of 84.1% for the MIT-BIH arrhythmia, and sensitivity of 93.9% and specificity of 84.4% for the clinical database. The advantage of using a short segment is more accurate AF burden calculation as the timing of transitions between NSR and AF are more accurately detected.

Multivariate ECG analysis for apnoea bradycardia detection and characterisation in preterm infants

An analysis of the information content of new features derived from the electrocardiogram of preterm infants is presented. Beat detection and segmentation methods, specifically adapted to this population through evolutionary algorithms, led to an improved sensitivity and positive predictive value. RR interval, R -wave amplitude and QRS duration time-series were extracted at rest ( T 1), before ( T 2), during ( T 3) and after ( T 4) apnoea-bradycardia episodes. Significant variations for T 1- T 2 vs. T 1- T 3 and T 1- T 3 vs. T 1- T 4 were observed. Results reveal changes in the R-wave amplitude and QRS duration at the onset and termination of apnoea-bradycardia episodes which could be useful for their detection and characterisation.

INTELLIGENT DIAGNOSIS OF CARDIOVASCULAR DISEASES UTILIZING ECG SIGNALS

Early automatic detection of cardiovascular diseases is of great importance to provide timely treatment and reduce fatality rate. Although many efforts have been devoted to detecting various arrhythmias, classification of other common cardiovascular diseases still lacks comprehensive and intensive studies. This work aims at developing an automatic diagnosis system for myocardial infarction, valvular heart disease, cardiomyopathy, hypertrophy, and bundle branch block, based on the clinic recordings provided by PTB Database. The proposed diagnosis system consists of the components as baseline wander reduction, beat segmentation, feature extraction, feature reduction and classification. The selected features are the location, amplitude and width of each wave, exactly the parameters of ECG dynamical model. We also propose a mean shift algorithm based method to extract these features. To demonstrate the availability and efficacy of the proposed system, we use a total of 13,564 beats to conduct a large scale experiment, where only 25% beats are utilized to train the eigenvectors of generalized discriminant analysis in the feature reduction phase and 25% beats are applied to train the support vector machine in the classification phase. The average sensitivity, specificity and positive predicitivity for the test set, containing 75% beats, are respectively 96.06%, 99.32% and 97.29%.

Development of ECG beat segmentation method by combining lowpass filter and irregular R–R interval checkup strategy

We have developed a long-term cardiorespiratory sensor system that includes a wearable sensor probe with adaptive hardware filters and data processing algorithms ( Choi & Jiang, 2006, 2008). However, the data processing algorithm proposed for the R–R interval (RRI) information extraction did not work well in the case of ECG signals with baseline shifts or muscle artifacts. Furthermore, many false ECG beats were extracted due to a weak decision-making scheme. Then, those false beats produced irregular RRI information and erroneous heart rate variability results. Modification of data processing algorithm was strongly needed. Therefore, this work presented an efficient ECG beat segmentation method using an irregular RRI checkup strategy into five sequential RRI patterns. This algorithm was comprised of signal processing stage and ECG beat detector stage. The signal processing included the wavelet denoising, the baseline shift elimination by 20 Hz lowpass filter and the envelope curve extraction by a single degree of freedom analytical model. The ECG beat detector included the candidate ECG beat detection and segmentation by one threshold and by irregular RRI checkup strategy, respectively. In particular, four abnormal RRI patterns were proposed to find out false ECG beats. The MIT-BIH arrhythmia database was selected as the dataset for testing the proposed algorithm. The proposed irregular RRI checkup strategy estimated 5463 beats to the suspected false beats and succeeded in segmenting 96.19% (5255 beats) of them. The performance results showed that our algorithm had very good results such as the detection error of 0.54%, sensitivity of 99.66% and positive predictivity of 99.80%. Furthermore, our algorithm showed very high accuracy as the mean time error between the beat annotations of the database and our obtained beat occurence times was 7.75 ms.

An Enhanced Automatic Algorithm for Estimation of Respiratory Variations in Arterial Pulse Pressure During Regions of Abrupt Hemodynamic Changes

We describe an improved automatic algorithm to estimate the pulse-pressure-variation (PPV) index from arterial blood pressure (ABP) signals. This enhanced algorithm enables for PPV estimation during periods of abrupt hemodynamic changes. Numerous studies have shown PPV to be one of most specific and sensitive predictors of fluid responsiveness in mechanically ventilated patients. The algorithm uses a beat detection algorithm to perform beat segmentation, kernel smoothers for envelope detection, and a suboptimal Kalman filter for PPV estimation and artifact removal. In this paper, we provide a detailed description of the algorithm and assess its performance on over 40 h of ABP signals obtained from 18 mechanically ventilated crossbred Yorkshire swine. The subjects underwent grade V liver injury after splenectomy, while receiving mechanical ventilation, and general anesthesia with isoflurane. All subjects in the database underwent a period of abrupt hemodynamic change after an induced grade V liver injury involving severe blood loss resulting in hemorrhagic shock, followed by fluid resuscitation with either 0.9% normal saline or lactated ringers solutions. Trained experts manually calculated PPV at five time instances during the period of abrupt hemodynamic changes. We report validation results comparing the proposed algorithm against a commercial system (pulse contour cardiac output, PICCO) with continuous PPV monitoring capabilities. Both systems were assessed during periods of abrupt hemodynamic changes against the "gold-standard" PPV, calculated and manually annotated by experts. Our results indicate that the proposed algorithm performs considerably better than the PICCO system during regions of abrupt hemodynamic changes.

A model-based Bayesian framework for ECG beat segmentation

The study of electrocardiogram (ECG) waveform amplitudes, timings and patterns has been the subject of intense research, for it provides a deep insight into the diagnostic features of the heart's functionality. In some recent works, a Bayesian filtering paradigm has been proposed for denoising and compression of ECG signals. In this paper, it is shown that this framework may be effectively used for ECG beat segmentation and extraction of fiducial points. Analytic expressions for the determination of points and intervals are derived and evaluated on various real ECG signals. Simulation results show that the method can contribute to and enhance the clinical ECG beat segmentation performance.

Incremental HMM training applied to ECG signal analysis

This work discusses the implementation of incremental hidden Markov model (HMM) training methods for electrocardiogram (ECG) analysis. The HMMs are used to model the ECG signal as a sequence of connected elementary waveforms. Moreover, an adaptation process is implemented to adapt the HMMs to the ECG signal of a particular individual. The adaptation training strategy is based on incremental versions of the expectation–maximization, segmental k-means and Bayesian approaches. Performance of the training methods was assessed through experiments considering the QT and ST–T databases. The results obtained show that the incremental training improves beat segmentation and ischemia detection performance with the advantage of low computational effort.

Combining Wavelet Transform and Hidden Markov Models for ECG Segmentation

This work aims at providing new insights on the electrocardiogram (ECG) segmentation problem using wavelets. The wavelet transform has been originally combined with a hidden Markov models (HMMs) framework in order to carry out beat segmentation and classification. A group of five continuous wavelet functions commonly used in ECG analysis has been implemented and compared using the same framework. All experiments were realized on the QT database, which is composed of a representative number of ambulatory recordings of several individuals and is supplied with manual labels made by a physician. Our main contribution relies on the consistent set of experiments performed. Moreover, the results obtained in terms of beat segmentation and premature ventricular beat (PVC) detection are comparable to others works reported in the literature, independently of the type of the wavelet. Finally, through an original concept of combining two wavelet functions in the segmentation stage, we achieve our best performances.

ECG signal analysis through hidden Markov models

This paper presents an original hidden Markov model (HMM) approach for online beat segmentation and classification of electrocardiograms. The HMM framework has been visited because of its ability of beat detection, segmentation and classification, highly suitable to the electrocardiogram (ECG) problem. Our approach addresses a large panel of topics some of them never studied before in other HMM related works: waveforms modeling, multichannel beat segmentation and classification, and unsupervised adaptation to the patient's ECG. The performance was evaluated on the two-channel QT database in terms of waveform segmentation precision, beat detection and classification. Our waveform segmentation results compare favorably to other systems in the literature. We also obtained high beat detection performance with sensitivity of 99.79% and a positive predictivity of 99.96%, using a test set of 59 recordings. Moreover, premature ventricular contraction beats were detected using an original classification strategy. The results obtained validate our approach for real world application.

High performance data compression method with pattern matching for biomedical ECG and arterial pulse waveforms

Biomedical waveforms, such as electrocardiogram (ECG) and arterial pulse, always possess a lot of important clinical information in medicine and are usually recorded in a long period of time in the application of telemedicine. Due to the huge amount of data, to compress the biomedical waveform data is vital. By recognizing the strong similarity and correlation between successive beat patterns in biomedical waveform sequences, an efficient data compression scheme mainly based on pattern matching is introduced in this paper. The waveform codec consists mainly of four units: beat segmentation, beat normalization, two-stage pattern matching and template updating and residual beat coding. Three different residual beat coding methods, such as Huffman/run-length coding, Huffman/run-length coding in discrete cosine transform domain, and vector quantization, are employed. The simulation results show that our compression algorithms achieve a very significant improvement in the performances of compression ratio and error measurement for both ECG and pulse, as compared with some other compression methods.

The efficient detection of Korotkoff sounds: Geddes, L.A., Moore, A.G. Medical and Biological Engineering, Vol 6, No 6 (November 1968) pp 603–609 (987)

QRS detection in the electrocardiogram signal is very crucial as a preliminary step for obtaining QRS complex, beat segmentation, and beat-to-beat intervals. Two main problems in QRS detection are a variety of noise types and various types of abnormal morphologies. We propose a QRS detection algorithm consisting of the quadratic filter for enhancing QRS signal to noise ratio. Results show that significant improvement in QRS signal to noise ratio can be obtained from challenging situations including low amplitude QRS complexes corrupted by baseline drift and abnormal morphologies such as an aberrated atrial premature beat, a premature ventricular contraction beat, a fusion of ventricular and normal beat, and a fusion of paced and normal beat. The enhancements in QRS signal to noise ratio allow us to use a single fixed threshold without any additional post-processing techniques in beat detection step. The performance of proposed algorithm was evaluated with the electrocardiogram data from MIT-BIH arrhythmia database. Results show that the quadratic filter is capable of enhancing QRS signal to noise very well leading to the average detection error rate of 0.38% from 48 records.