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.

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Standard 12-lead (S12) system and Mason-Likar 12-lead (ML12) system despite of being most acceptable systems for clinical usage are not the preferred lead systems for remote monitoring (RM) applications. Usually RM applications involve wireless transmission of signals and a 2-3 lead system is preferred for bandwidth and storage limitations and data transmission time. Generally, ECG compression techniques are applied for the same, however, compression ratio (CR) depends on the number of channels and decreases with the increase in number of channels. Thus, it facilitates the usage of a 2-3 lead system. However, a reduced lead (RL) system with 2-3 leads may be inadequate for the information desired by the cardiologists who are accustomed to S12 or ML12 system pertaining to its decades old usage. In this paper, we attempt to provide solution to both technical and non-technical limitations of RM applications. We reconstruct S12 and ML12 systems from Reduced 3-lead (R3L) system comprising of basis leads I, II, V2 using personalized or patient-specific transformation. Two separate investigations have been carried out for S12 and ML12 with their corresponding R3L systems comprising of their respective basis leads. PhysioNet PTBDB and INCARTDB after wavelet based preprocessing were used in this investigation. R 2 statistics, correlation (rx) and regression (bx) coefficients were used to evaluate reconstructed signal against the original signal and the mean values obtained were 96.53%, 0.982 and 0.968 (S12) and 96.53%, 0.982 and 0.968 (ML12) respectively. R3L system reduces number of leads and electrodes from 12 and 10 to 3 and 5 respectively, lowers bandwidth and storage requirements, data transmission time and increases CR. The study shows that basis leads obtained from S12 outperforms the basis leads of ML12 for reconstruction of precordial leads.

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