Tele-cardiology sensor networks for remote ECG monitoring

One of today’s most pressing matters in medical care is the response time to patients in need. The scope of this thesis is to suggest a solution that would help reduce response time in emergency situations utilizing wireless sensor networks technology. Wireless sensor network researches have recently gained unprecedented momentum in both industries and academia, especially its potential applications in Emergency Medical Services and Intensive Care Units. The enhanced power efficiency, minimized production cost, condensed physical layout, as well as reduced wired connections, presents a much more proficient and simplified approach to the continuous monitoring of patients’ physiological status. This thesis focuses on the areas of remote ECG feature extraction utilizing wavelet transformation concepts and sensor networks technology. The proposed sensor network system provides the following contributions. The low-cost, low-power wearable platforms are to be distributed to patients of concern and will provide continuous ECG monitoring by measuring electrical potentials between various points of the body using a galvanometer. The system is enabled with integrated RF communication capability that will relay the signals wirelessly to a workstation monitor. The workstation is equipped with ECG signal processing software that performs ECG characteristic extractions via wavelet transformation. Lastly, a low-complex, end-to-end security scheme is also incorporated into this system to ensure patient privacy. Other notable features include location tracking algorithms for patient tracking, and MATLAB Server environment for internal communication

ECG-Waves: Analysis and Detection by Continuous Wavelet Transform

In this work, we have developed a new algorithm for electrocardiogram (ECG) features extraction. This algorithm was based on continuous wavelet transform (CWT). The core of the process involved analyzing the signal using the CWT coefficients with a selection of scale parameter corresponding to each ECG wave. The entry point of our method was the R peak detection. The next step was the Q and S point localization, after we identified the P and T waves. We evaluated our algorithm on apnea and MIT-BIH databases recording. The algorithm achieved a good performance with the sensitivity of 99.84 % and the positive predictive value of 99.53 %

Analysis of the high-frequency content in human qrs complexes by the continuous wavelet transform: An automatized analysis for the prediction of sudden cardiac death

Background: Fragmentation and delayed potentials in the QRS signal of patients have been postulated as risk markers for Sudden Cardiac Death (SCD). The analysis of the high-frequency spectral content may be useful for quantification. Methods: Forty-two consecutive patients with prior history of SCD or malignant arrhythmias (patients) where compared with 120 healthy individuals (controls). The QRS complexes were extracted with a modified Pan-Tompkins algorithm and processed with the Continuous Wavelet Transform to analyze the high-frequency content (85–130 Hz). Results: Overall, the power of the high-frequency content was higher in patients compared with controls (170.9 vs. 47.3 103nV2Hz−1; p = 0.007), with a prolonged time to reach the maximal power (68.9 vs. 64.8 ms; p = 0.002). An analysis of the signal intensity (instantaneous average of cumulative power), revealed a distinct function between patients and controls. The total intensity was higher in patients compared with controls (137.1 vs. 39 103nV2Hz−1s−1; p = 0.001) and the time to reach the maximal intensity was also prolonged (88.7 vs. 82.1 ms; p < 0.001). Discussion: The high-frequency content of the QRS complexes was distinct between patients at risk of SCD and healthy controls. The wavelet transform is an efficient tool for spectral analysis of the QRS complexes that may contribute to stratification of risk