An automated algorithm for online detection of fragmented QRS and identification of its various morphologies

Abstract

Fragmented QRS (f-QRS) has been proven to be an efficient biomarker for several diseases, including remote and acute myocardial infarction, cardiac sarcoidosis, non-ischaemic cardiomyopathy, etc. It has also been shown to have higher sensitivity and/or specificity values than the conventional markers (e.g. Q-wave, ST-elevation, etc.) which may even regress or disappear with time. Patients with such diseases have to undergo expensive and sometimes invasive tests for diagnosis. Automated detection of f-QRS followed by identification of its various morphologies in addition to the conventional ECG feature (e.g. P, QRS, T amplitude and duration, etc.) extraction will lead to a more reliable diagnosis, therapy and disease prognosis than the state-of-the-art approaches and thereby will be of significant clinical importance for both hospital-based and emerging remote health monitoring environments as well as for implanted ICD devices. An automated algorithm for detection of f-QRS from the ECG and identification of its various morphologies is proposed in this work which, to the best of our knowledge, is the first work of its kind. Using our recently proposed time -domain morphology and gradient-based ECG feature extraction algorithm, the QRS complex is extracted and discrete wavelet transform (DWT) with one level of decomposition, using the 'Haar' wavelet, is applied on it to detect the presence of fragmentation. Detailed DWT coefficients were observed to hypothesize the postulates of detection of all types of morphologies as reported in the literature. To model and verify the algorithm, PhysioNet's PTB database was used. Forty patients were randomly selected from the database and their ECG were examined by two experienced cardiologists and the results were compared with those obtained from the algorithm. Out of 40 patients, 31 were considered appropriate for comparison by two cardiologists, and it is shown that 334 out of 372 (89.8%) leads from the chosen 31 patients complied favourably with our proposed algorithm. The sensitivity and specificity values obtained for the detection of f-QRS were 0.897 and 0.899, respectively. Automation will speed up the detection of fragmentation, reducing the human error involved and will allow it to be implemented for hospital-based remote monitoring and ICD devices.

ECG Abnormality Detection Algorithm

“…The best accuracy they reached was 100% with Multilayer Perceptron (MLP) [5]. Maheshwari et al developed a novel algorithm to detect the fragmentation of QRS complex in an ECG signal [3]. To verify the validity of their developed algorithm they used PTB database from physioNet [3].…”

“…However, for this solution to be viable, an automated, simple and efficient heart's abnormalities detection algorithms must be implemented [3]. Those algorithms can be used extensively in eHealth systems and applications and ported to mobiles or sensors processing units [3]. Many ECG classification algorithms were implemented recently [7].…”

“…The massive prevalence of heart diseases worldwide urges for novel health solutions. Among the prominent emerging solutions are non-invasive heart rate monitoring technologies and remote health monitoring systems (eHealth systems) [3], [4].…”

ECG fiducial point extraction using switching Kalman filter

“…FP extraction has been used as a preprocessing step in several applications such as detection of fragmented QRS complex [25], mobile health care applications [26], "Selvester QRS scoring" system [27], ischemia detection [28], ECG-based subject identification system [29] and biometric recognition based on fusion of ECG and EEG 15 signals [30,31].…”

“…SKF is used for several applications such as figure tracking [35], acoustic segmentation [36], contour tracking in clutter [37], modeling and detecting motor cortical 25 activity [38], prediction and tracking an adaptive meterological sensing network [39], tracking and event detection at traffic intersections [40], ECG ventricular beat classification [41] and finally for apnea bradycardia detection from ECG signals [42].…”