Muhammad Hassan, Sami-ur Rahman, Fakhre Alam, Zafar Ali, Saeed Alam.
Effect Of Rising Habit On Human Health Using ECG Signals.
J Postgrad Med Inst Jan ;30(4):364-70.

Objective: To find out the effect of rising habit on human health using ECG signals. Methodology: One hundred male individuals (age 20 to 35 years) have been selected for the study. The morning rising habit of these individuals was different. The study was conducted at University of Malakand in collaboration with Ali Clinical Laboratory, Lower Dir. The duration of the study was from August-2015 to November- 2015. The electrocardiogram (ECG) signals were classified to find out patterns in heart rhythm of early and late risers. An artificial neural network based classifier named Multi-Layer Perceptron (MLP) was used for the classification. The ECG signals were obtained and on the bases of ECG patterns, the individuals were classified into two groups i.e. early and late risers. The classifier was trained on 70% samples and was tested on 30% of the data set. Results: The proposed classification shows 83 % accuracy. Late risers have more probability of different abnormalities. The QRS duration was normal for 80% samples of the early risers while it was normal for only 37% samples of the late risers. Similarly, QTc interval was normal for 80% samples of the early risers while it was normal for only 40% samples of the of later risers. There were 20% abnormal values for early risers and 60% abnormal values for late risers in their QTc intervals. Conclusion: Earlier risers are healthier than the late risers based on their ECG pattern as well as on the number of normal ECG features.

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