Anik Lestari

Premature ventricular contraction detection using swarm-based support vector machine and QRS wave features

A novel strategy for detecting Premature Ventricular Contraction (PVC) is proposed and investigated. The strategy employs a Swarm-based Support Vector Machine (SSVM). An SSVM is an SVM optimised by using Particle Swarm Optimisation (PSO). The strategy proposes new inputs. The inputs involve the width and the gradient of the electrocardiographic QRS wave. Experiments with different inputs and different SVM kernel functions are conducted to find the best one for PVC detection. On a test using clinical data, SSVM performs well in PVC detection with sensitivity, specificity and accuracy of 98.94%, 99.99% and 99.46%, respectively.

FACTORS ASSOCIATED WITH THE QUALITY OF LIFE AMONG THE ELDERLY

Background: Most countries in the world have succesfully prolong life expectancy of their populations. However, the quality of life may be decreasing with increasing age. This study aimed to investigate factors associated with the quality of life of the elderly. Subjects and Method: This was a cross sectional study carried out in Surakarta, Sragen, Karanganyar, and Klaten, in Central Java, from January to March 2017. A total of 224 elderlies were selected for this study. The dependent variable was quality of life. The independent variables were age, education, income, behavior, locus of control, family support, peer support, and social support. Data were collected by questionnaire and analyzed by path analysis. Results: Quality of life among the elderly showed positive association with education ≥SMA (b= 0.43; SE= 0.43; p= 0.668), income ≥Rp 876,420 (b= 0.92; SE<0.001; p= 0.357), positive behavior (b= 2.07; SE= 0.18; p= 0.039), and peer support (b= 7.35; SE= 0.22; p<0.001). Quality of life among the elderly showed negative association with age (b= -1.06; SE= 0.05; p= 0.290) and external locus of control (b= -1.07; SE= 0.25; p= 0.284). Conclusion: Quality of life among the elderly increases with higher education ≥SMA, higher income, positive behavior, and peer support. Quality of life decreases with increasing age and external locus of control. Keywords: quality of life, age, education, income, peer support, locus of control, elderly

Pembuatan Elektrokardiogram dan Penentuan Interval QRS secara Otomatis

Pada penelitian ini telah dibuat rancangan sistem elektrokardiogram (EKG) yang dilengkapi dengan sistem penentuan interval QRS secara otomatis. Sistem EKG yang dibuat menggunakan arduino dan komputer. Hasil dari penelitian ini diimplementasikan menggunakan perangkat lunak processing. Data sinyal jantung yang digunakan pada penelitian ini diambil langsug dari pasien. Untuk menentukan interval QRS maka terlebih dahulu dilakukan penentuan posisi puncak R. Posisi puncak R selanjutnya digunakan untuk menentukan posisi titik Q dan posisi titik S. Hasil akurasi penentuan interval QRS pada penelitian ini adalah sebesar 95,89%. ABSTRACT In this research, system design electrocardiogram (EKG) that is equipped with a QRS interval determination system automatically. ECG systems created using arduino and computer. The results of this research are implemented using software processing. Cardiac signal data used in this research were taken of patients. To determine the QRS interval, the first is the determination of the peak position R. Peak position R is then used to determine the position of the point Q and the position of the point S. Result for accuracy determination QRS interval in this research amounted to 95.89%.

Atrial fibrillation detection using support vector machine and electrocardiographic descriptive statistics

This paper proposes a new technique for detecting atrial fibrillation (AF). The method employs electrocardiographic features and support vector machine (SVM). The features include descriptive statistics of electrocardiographic RR interval. The RR interval is the distance in time between two consecutive R-peaks of electrocardiogram. AF detections using SVM with different electrocardiographic features and different SVM free parameters are explored. Employing SVM with the optimal free parameters and all the proposed electrocardiographic features, we find an AF detection technique with a comparable performance. The best performance obtained by the technique is 98.47% and 97.84%, in terms of sensitivity and specificity.

Premature ventricular contraction detection using artificial neural network developed in android application

We have conducted a study of detection system for premature ventricular contraction (PVC) developed in an android mobile phone. The system utilizes artificial neural network (ANN) with electrocardiographic (ECG) features of RR interval and QRS width. RR Interval and QRS width is Interval in ECG waveform. The algorithms of the detection are implemented using JAVA Eclipse Juno. The system is examined using electrocardiography of patients provided by Physionet MIT-BIH. The feature number is varied and the best result is found when both features RR interval and QRS width are applied with the performances of 94.58%, 96.59% and 96.29% in terms of sensitivity, specificity and accuracy.

Atrial fibrillation detection using support vector machine

This article introduces a new method for detection of atrial fibrillation (AFib) using a support vector machine (SVM). AFib could lead to heart failure and stroke and thus an AFib early detection is very important. In this article, an SVM and variabilities of electrocardiographic heart rate are employed to detect AFib. Radial basis functions (RBF) is utilized for SVM. Different SVM constructions are tested to find the best one. Furthermore, two features of electrocardiogram are examined as the inputs of SVM. Using clinical electrocardiogram, the proposed method find the performance of 95.81 %, 98.44% and 97.50% in terms of sensitivity, specificity and accuracy.