EFFECT OF RISING HABIT ON HUMAN HEALTH USING ECG SIGNALS

Main Article Content

Muhammad Hassan
Sami Ur Rahman
Fakhre Alam
Zafar Ali
Saeed Alam

Abstract

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.

Article Details

How to Cite
1.
Hassan M, Rahman SU, Alam F, Ali Z, Alam S. EFFECT OF RISING HABIT ON HUMAN HEALTH USING ECG SIGNALS. J Postgrad Med Inst [Internet]. 2016 Nov. 26 [cited 2022 May 21];30(4). Available from: https://jpmi.org.pk/index.php/jpmi/article/view/1975
Section
Original Article
Author Biographies

Muhammad Hassan, Depart of Computer Science & IT, University of Malakand, KP Pakistan

Department of Computer Science & IT, Research Scholar

Sami Ur Rahman, Depart of Computer Science & IT, University of Malakand, KP Pakistan

Depart of Computer Science & IT, Assistant Professor

Fakhre Alam, Depart of Computer Science & IT, University of Malakand, KP Pakistan

Depart of Computer Science & IT, Lecturer

Zafar Ali, Department of Medicine, Medical Teaching Institute, Lady Reading Hospital, Peshawar - Pakistan.

Department of Medicine, Assistant Professor

Saeed Alam, Department of Cardiology, Lady Reading Hospital, Peshawar, Pakistan

Department of Cardiology, Trinee Medical officer

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