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Abstract

Objective: To find out the effect of rising habit on human health using ECGsignals.Methodology: One hundred male individuals (age 20 to 35 years) have beenselected for the study. The morning rising habit of these individuals was different.The study was conducted at University of Malakand in collaborationwith Ali Clinical Laboratory, Lower Dir. The duration of the study was fromAugust-2015 to November- 2015. The electrocardiogram (ECG) signals wereclassified to find out patterns in heart rhythm of early and late risers. An artificialneural network based classifier named Multi-Layer Perceptron (MLP) wasused for the classification. The ECG signals were obtained and on the basesof ECG patterns, the individuals were classified into two groups i.e. early andlate 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 havemore probability of different abnormalities. The QRS duration was normal for80% samples of the early risers while it was normal for only 37% samples ofthe late risers. Similarly, QTc interval was normal for 80% samples of the earlyrisers while it was normal for only 40% samples of the of later risers. Therewere 20% abnormal values for early risers and 60% abnormal values for laterisers in their QTc intervals.Conclusion: Earlier risers are healthier than the late risers based on their ECGpattern as well as on the number of normal ECG features.