Background: Cardiac disease is one of the main causes of catastrophic mortality.\nTherefore, detecting the symptoms of cardiac disease as early as possible is important\nfor increasing the patient�s survival. In this study, a compact and effective architecture\nfor detecting atrial fibrillation (AFib) and myocardial ischemia is proposed. We\ndeveloped a portable device using this architecture, which allows real-time\nelectrocardiogram (ECG) signal acquisition and analysis for cardiac diseases.\nMethods: A noisy ECG signal was preprocessed by an analog front-end consisting of\nanalog filters and amplifiers before it was converted into digital data. The analog\nfront-end was minimized to reduce the size of the device and power consumption by\nimplementing some of its functions with digital filters realized in software. With the\nECG data, we detected QRS complexes based on wavelet analysis and feature\nextraction for morphological shape and regularity using an ARM processor. A classifier\nfor cardiac disease was constructed based on features extracted from a training dataset\nusing support vector machines. The classifier then categorized the ECG data into\nnormal beats, AFib, and myocardial ischemia.\nResults: A portable ECG device was implemented, and successfully acquired and\nprocessed ECG signals. The performance of this device was also verified by comparing\nthe processed ECG data with high-quality ECG data from a public cardiac database.\nBecause of reduced computational complexity, the ARM processor was able to process\nup to a thousand samples per second, and this allowed real-time acquisition and\ndiagnosis of heart disease. Experimental results for detection of heart disease showed\nthat the device classified AFib and ischemia with a sensitivity of 95.1% and a specificity\nof 95.9%.\nConclusions: Current home care and tele medicine systems have a separate device\nand diagnostic service system, which results in additional time and cost. Our proposed\nportable ECG device provides captured ECG data and suspected waveform to identify\nsporadic and chronic events of heart diseases. This device has been built and evaluated\nfor high quality of signals, low computational complexity, and accurate detection.
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