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All Journal ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektronika Jurnal Rekayasa elektrika
ERVIN MASITA DEWI
Politeknik Negeri Bandung
Computer Science & IT Control & Systems Engineering Electrical & Electronics Engineering Energy Engineering

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Multi-Abnormal ECG Signal Classification using Dispersion Entropy and Statistic Feature ERVIN MASITA DEWI; SUCI AULIA; SUGONDO HADIYOSO
Jurnal Elkomika Vol 10, No 3 (2022): ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektr
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v10i3.677

Abstract

ABSTRAKElektrokardiogram (EKG) adalah salah satu perangkat medis yang paling banyak digunakan untuk mendiagnosis masalah jantung. Sinyal abnorma EKG mempunyai variasi dan beberapa mirip antara yang satu dengan lainnya. Oleh karena itu, pada penelitian ini diusulkan metode klasifikasi kelainan jantung berdasarkan EKG menggunakan fitur statistik orde satu dan Dispersion Entropy (DisEn) untuk tahap ekstraksi ciri. Sedangkan untuk tahap klasifikas sinyal EKG multi-abnormal, kami membandingkan metode Support Vector Machine (SVM) dan K-Nearest Neighbor (KNN). Pada penelitian ini diklasifikasikan tujuh kelas EKG, yaitu Normal, Atrial Fibrillation (AFIB), Atrial Flutter (AFL), Atrial Premature Beats (APB), Begiminy, Left Bundle Branch Block (LBBB), dan Premature Ventricular Contraction (PVC). Dari simulasi ini, sistem dapat mendeteksi sinyal normal dan abnormal dengan akurasi 85,1% menggunakan K-NN. Sementara itu, pada simulasi klasifikasi tujuh kelas sinyal EKG menghasilkan akurasi hingga 75.1%.Kata kunci: EKG, klasifikasi, Dispersion Entropy, statistik ABSTRACTElectrocardiogram (ECG) is one of the most widely used medical devices to diagnose heart disease. Abnormal ECG signals have variations and some are similar to another. Therefore, in this study, proposed a method for classifying cardiac abnormalities based on ECG using first-order statistical features and Dispersion Entropy (DisEn) for feature extraction. Meanwhile, for the multiabnormal ECG signal classification stage, we compared the Support Vector Machine (SVM) and K-Nearest Neighbor (KNN) methods. In this study, seven ECG classes were classified, namely Normal, Atrial Fibrillation (AFIB), Atrial Flutter (AFL), Atrial Premature Beats (APB), Begiminy, Left Bundle Branch Block (LBBB), and Premature Ventricular Contraction (PVC). From this simulation, the system can detect normal and abnormal signals with an accuracy of 85.1% using K-NN. Meanwhile, the classification simulation of seven classes of ECG signals produces an accuracy of up to 75.1%.Keywords: ECG, classification, Dispersion Entropy, statistics
Measurement of Ankle Brachial Index with Oscillometric Method for Early Detection of Peripheral Artery Disease Ervin Masita Dewi; Gema Ramadhan; Robinsar Parlindungan; Lenny Iryani; Trisno Yuwono
Jurnal Rekayasa Elektrika Vol 18, No 2 (2022)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (1393.059 KB) | DOI: 10.17529/jre.v18i2.25758

Abstract

Peripheral Arterial Disease (PAD) is a blood vessel disease caused by blockage or plaque accumulation around the artery walls. PAD is included in the category of diseases that are often diagnosed too late and affect more severe cases, such as the death of certain tissues or body parts. The Ankle Brachial Index (ABI) is an accurate non-invasive method for diagnosing PAD, in practice, ABI is usually performed in certain hospitals and is still difficult to find due to limited tools. Therefore, a tool is made that can detect the condition of a person's PAD based on the ABI value. The tool is made using two MPX5050GP sensors to detect oscillometric pulses, a DC pump and solenoid valve as an actuator to pump and deflate the cuff, ADS1115 as an external ADC to increase the accuracy of sensor readings, as well as an LCD and buzzer as tool indicators. The output is displayed in the form of a print out from a thermal printer, with an emergency stop that functions as a safety system to power off the supply when a failure occurs in the measurement process. Oscillometric method is used to detect systolic and diastolic pressure. The accuracy of the tool is 95.5%. This accuracy result is obtained by comparing the readings of systolic and diastolic values using a sphygmomanometer which is commonly used.
IoT-Based Early Detection of Cardiovascular Disease with Ankle Brachial Index Measurement for Right and Left Body Simultaneously ERVIN MASITA DEWI; AWAN WAHYU SETIAWAN; SUGONDO HADIYOSO
Jurnal Elkomika Vol 11, No 4 (2023): ELKOMIKA: Jurnal Teknik Energi Elektrik, Teknik Telekomunikasi, & Teknik Elektr
Publisher : Institut Teknologi Nasional, Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.26760/elkomika.v11i4.1032

Abstract

ABSTRAKDeteksi dini penyakit kardiovaskular sangat diperlukan untuk mengurangi risiko kematian. Deteksi dini penyakit kardiovaskular dapat dilakukan dengan bermacammacam metode, salah satunya adalah menggunakan metode Ankle Brachial Indeks (ABI). Metode ini membandingkan tekanan darah antara sistole pada bagian tangan dan kaki secara bersamaan. Pada penelitian ini dibuatlah alat pengukur ABI yang dapat mengukur secara serempak antara bagian tubuh kanan dan kiri, yaitu merupakan pengembangan dari penelitian sebelumnya yang hanya dapat melakukan pengukuran pada satu sisi tubuh saja. Dengan pengukuran secara serempak, diharapkan hasil yang diperoleh lebih akurat dan lebih efektif. Hasil validasi dari alat ini setelah dibandingkan dengan sphygmomanometer memiliki akurasi sebesar 96.6%. Selain itu data riwayat pemeriksaan dapat disimpan dan diakses oleh pasien dan dokter melalui teknologi IoT.Kata kunci: deteksi dini, kardiovaskular, Ankle Brachial Indeks, IoT ABSTRACTEarly detection of Cardiovascular Disease (CVD) is needed to reduce the risk of death. Early detection of cardiovascular disease can be done using various methods, one of which is the Ankle Brachial Index (ABI) method. This method compares blood pressure between systoles on the hands and feet simultaneously. In this study, the ABI measuring instrument was made that could simultaneously measure the right and left parts of the body, a development from previous research that could only take measurements on one side of the body. With simultaneous measurements, the results will be more accurate and effective. The validation results of this tool, when compared with the sphygmomanometer, have an accuracy of 96.6%. Besides, patients and doctors can store and access examination history data through IoT platform.Keywords: early detection, cardiovascular, Ankle Brachial Indeks, IoT
Measurement of Ankle Brachial Index with Oscillometric Method for Early Detection of Peripheral Artery Disease Ervin Masita Dewi; Gema Ramadhan; Robinsar Parlindungan; Lenny Iryani; Trisno Yuwono
Jurnal Rekayasa Elektrika Vol 18, No 2 (2022)
Publisher : Universitas Syiah Kuala

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.17529/jre.v18i2.25758

Abstract

Peripheral Arterial Disease (PAD) is a blood vessel disease caused by blockage or plaque accumulation around the artery walls. PAD is included in the category of diseases that are often diagnosed too late and affect more severe cases, such as the death of certain tissues or body parts. The Ankle Brachial Index (ABI) is an accurate non-invasive method for diagnosing PAD, in practice, ABI is usually performed in certain hospitals and is still difficult to find due to limited tools. Therefore, a tool is made that can detect the condition of a person's PAD based on the ABI value. The tool is made using two MPX5050GP sensors to detect oscillometric pulses, a DC pump and solenoid valve as an actuator to pump and deflate the cuff, ADS1115 as an external ADC to increase the accuracy of sensor readings, as well as an LCD and buzzer as tool indicators. The output is displayed in the form of a print out from a thermal printer, with an emergency stop that functions as a safety system to power off the supply when a failure occurs in the measurement process. Oscillometric method is used to detect systolic and diastolic pressure. The accuracy of the tool is 95.5%. This accuracy result is obtained by comparing the readings of systolic and diastolic values using a sphygmomanometer which is commonly used.
Co-Authors AWAN WAHYU SETIAWAN Gema Ramadhan Lenny Iryani ROBINSAR PARLINDUNGAN Suci Aulia Sugondo Hadiyoso Trisno Yuwono