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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Fragmented QRS (f-QRS) has been found to have higher sensitivity and/or specificity values for several diseases including remote and acute myocardial infarction, cardiac sarcoidosis etc, compared to other conventional bio-markers viz. Q-wave, ST-elevation etc. Several of these diseases do not have a reliable bio-marker and hence, patients suffering from them have to undergo expensive and sometimes invasive tests for diagnosis viz. myocardial biopsy, cardiac catheterization etc. This paper proposes automation of fragmentation detection which will lead to a more reliable diagnosis and therapy reducing human error, time consumption and thereby alleviating the need of enormous training required for detection of fragmentation. In this paper, we propose a novel approach to detect the discontinuities present in QRS complex of standard 12-lead ECG, known as fragmented QRS, using Discrete Wavelet transform (DWT) targeting both hospital-based and remote health monitoring environments. Fragmentation Detection Algorithm (FDA) was designed and modeled using PhysioNet's PTBDB and upon reiterative refinements it successfully detected all discontinuities in the QRS complex. The QRS complexes of 31 patients obtained randomly from PhysioNet's PTBDB were examined by two experienced cardiologists and the measurements obtained were compared with the results of our proposed FDA leading to 89.8% agreement among them.

Genomics has the potential to transform medicine from reactive to a personalized, predictive, preventive and participatory (P4) form. Being a Big Data application with continuously increasing rate of data production, the computational costs of genomics have become a daunting challenge. Most modern computing systems are heterogeneous consisting of various combinations of computing resources, such as CPUs, GPUs and FPGAs. They require platform-specific software and languages to program making their simultaneous operation challenging. Existing read mappers and analysis tools in the whole genome sequencing (WGS) pipeline do not scale for such heterogeneity. Additionally, the computational cost of mapping reads is high due to expensive dynamic programming based verification, where optimized implementations are already available. Thus, improvement in filtration techniques is needed to reduce verification overhead. To address the aforementioned limitations with regards to the mapping element of the WGS pipeline, we propose a Cross-platfOrm Read mApper using opencL (CORAL). CORAL is capable of executing on heterogeneous devices/platforms simultaneously. It can reduce computational time by suitably distributing the workload without any additional programming effort. We showcase this on a quadcore Intel CPU along with two Nvidia GTX 590 GPUs, distributing the workload judiciously to achieve up to 2× speedup compared to when only CPUs are used. To reduce the verification overhead, CORAL dynamically adapts k-mer length during filtration. We demonstrate competitive timings in comparison with other mappers using real and simulated reads. CORAL

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