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All Journal JURNAL NASIONAL TEKNIK ELEKTRO Indonesian Journal of Electrical Engineering and Computer Science
Eileen Su Lee Ming
Universiti Teknologi Malaysia
Electrical & Electronics Engineering

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Objective assessment of surgeon’s psychomotor skill using virtual reality module Siti Nor Zawani Ahmmad; Eileen Su Lee Ming; Yeong Che Fai; Suneet Sood; Anil Gandhi; Nur Syarafina Mohamed; Hisyam Abdul Rahman; Etienne Burdet
Indonesian Journal of Electrical Engineering and Computer Science Vol 14, No 3: June 2019
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v14.i3.pp1533-1543

Abstract

This study aims to identify measurable parameters that could be used as objective assessment parameters to evaluate surgical dexterity using computer-based assessment module. A virtual reality module was developed to measure dynamic and static hand movements in a bimanual experimental setting. The experiment was conducted with sixteen subjects divided into two groups: surgeons (N = 5) and non-surgeons (N = 11). Results showed that surgeons outperformed the non-surgeons in motion path accuracy, motion path precision, economy of movement, motion smoothness, end-point accuracy and end-point precision. The six objective parameters can complement existing assessment methods to better quantify a trainee’s performance. These parameters also could provide information of hand movements that cannot be measured with the human eye. An assessment strategy using appropriate parameters could help trainees learn on computer-based systems, identify their mistakes and improve their skill towards the competency, without relying too much on bench models and cadavers.
Studi Autoencoder Deep Learning pada Sinyal EKG Dandi Mochamad Reza; Satria Mandala; Salim M. Zaki; Eileen Su Lee Ming
JURNAL NASIONAL TEKNIK ELEKTRO Vol 12, No 3: November 2023
Publisher : Jurusan Teknik Elektro Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jnte.v12n3.1117.2023

Abstract

Arrhythmia refers to an irregular heart rhythm resulting from disruptions in the heart's electrical activity. To identify arrhythmias, an electrocardiogram (ECG) is commonly employed, as it can record the heart's electrical signals. However, ECGs may encounter interference from sources like electromagnetic waves and electrode motion. Several researchers have investigated the denoising of electrocardiogram signals for arrhythmia detection using deep autoencoder models. Unfortunately, these studies have yielded suboptimal results, indicated by low Signal-to-Noise Ratio (SNR) values and relatively large Root Mean Square Error (RMSE). This study addresses these limitations by proposing the utilization of a Deep LSTM Autoencoder to effectively denoise ECG signals for arrhythmia detection. The model's denoising performance is evaluated based on achieved SNR and RMSE values. The results of the denoising evaluations using the Deep LSTM Autoencoder on the AFDB dataset show SNR and RMSE values of 56.16 and 0.00037, respectively. Meanwhile, for the MITDB dataset, the corresponding values are 65.22 and 0.00018. These findings demonstrate significant improvement compared to previous research. However, it's important to note a limitation in this study—the restricted availability of arrhythmia datasets from MITDB and AFDB. Future researchers are encouraged to explore and acquire a more extensive collection of arrhythmia data to further enhance denoising performance.
Co-Authors Anil Gandhi Dandi Mochamad Reza Etienne Burdet Hisyam Abdul Rahman Nur Syarafina Mohamed Salim M. Zaki Satria Mandala Siti Nor Zawani Ahmmad Suneet Sood Yeong Che Fai