A Framework for Remote Patient Monitoring to Diagnose the Cardiac Disorders

Abstract

Electrocardiogram (ECG) is an efficient diagnostic tool to monitor the electrical activity of heart. One of the most vital benefit of using telecommunication technologies in medical field is to provide cardiac health care at a distance. Telecardiology is the most
efficient way to provide faster and affordable health care for the cardiac patients located at rural areas. Early detection of cardiac disorders can minimize cardiac death rates. In real time monitoring process, ECG data from a patient usually takes large storage space in the order of gigabytes (GB). Hence, compression of bulky ECG signal is a common requirement for faster transmission of cardiac signals using wireless technologies. Several
techniques such as the Fourier transform based methods, wavelet transform based methods, etc., have been reported for compression of ECG data. Though Fourier transform
is suitable for analyzing the stationary signals. An improved version, the wavelet transform allows the analysis of non-stationary signal. It provides a uniform resolution for all the scales, however, wavelet transform faces difficulties like uniformly poor resolution
due to limited size of the basic wavelet function and it is nonadaptive in nature. A data adaptive method to analyse non-stationary signal is based on empirical mode
decomposition (EMD), where the bases are derived from the multivariate data which are nonlinear and non-stationary. A new ECG signal compression technique based on
EMD is proposed, in which first EMD technique is applied to decompose the ECG signal into several intrinsic mode functions (IMFs). Next, downsampling, discrete cosine transform (DCT), window filtering and Huffman encoding processes are used sequentially to compress the ECG signal. The compressed ECG is then transmitted as short messageservice (SMS) message using a global system for mobile communications (GSM) modem. First the AT-command ‘+CMGF’ is used to set the SMS to text mode. Next, the GSM modem uses the AT-command ‘+CMGS’ to send a SMS message. The received text SMS messages are transferred to a personal computer (PC) using blue-tooth. All text
SMS messages are combined in PC as per the received sequence and fed as data input to decompress the compressed ECG data. The decompression method which is used to reconstruct the original ECG signal consists of Huffman decoding, inverse discrete cosine transform (IDCT) and spline interpolation. The performance of the compression and decompression techniques are evaluated in terms of compression ratio (CR) and percent root mean square difference (PRD) respectively by using both European ST-T database and Massachusetts Institute of Technology-Beth Israel Hospital (MIT-BIH) arrhythmia
database. The average values of CR and PRD for selected ECG records of European ST-T database are found to be 23.5:1 and 1.38 respectively. All 48 ECG records of
MIT-BIH arrhythmia database are used for comparison purpose and the average values of CR and PRD are found to be 23.74:1 and 1.49 respectively. The reconstructed
ECG signal is then used for detection of cardiac disorders like bradycardia, tachycardia and ischemia. The preprocessing stage of the detection technique filters the normalized signal to reduce noise components and detects the QRS-complexes. Next, ECG feature extraction, ischemic beat classification and ischemic episode detection processes are applied sequentially to the filtered ECG by using rule based medical knowledge. The
ST-segment and T-wave are the two features generally used for ischemic beat classification. As per the recommendation of ESC (European Society of cardiology) the ischemic episode detection procedure considers minimum 30s duration of signal. The performance of the ischemic episode detection technique is evaluated in terms of sensitivity (Se) and positive predictive accuracy (PPA) by using European ST-T database. This technique achieves an average Se and PPA of 83.08% and 92.42% respectively.