Abnormal Pattern Detection In Ppg Signals Using Time Series Analysis

The photoplethysmogram (PPG) signal is a data in continuous real-time series. It depicts the peripheral pulse wave that is produced due to heart activity, respiration, and other physiological effects. The time-series signal contains a lot of information which is difficult to be processed. The abnorm...

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Main Author: Siti Nur Hidayah, Mazelan
Format: Undergraduates Project Papers
Language:English
Published: 2022
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Online Access:http://umpir.ump.edu.my/id/eprint/39894/1/EA18084_Hidayah_ThesisV2%20-%20Hidayah%20Mazelan.pdf
http://umpir.ump.edu.my/id/eprint/39894/
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spelling my.ump.umpir.398942024-01-08T06:36:02Z http://umpir.ump.edu.my/id/eprint/39894/ Abnormal Pattern Detection In Ppg Signals Using Time Series Analysis Siti Nur Hidayah, Mazelan TA Engineering (General). Civil engineering (General) TK Electrical engineering. Electronics Nuclear engineering The photoplethysmogram (PPG) signal is a data in continuous real-time series. It depicts the peripheral pulse wave that is produced due to heart activity, respiration, and other physiological effects. The time-series signal contains a lot of information which is difficult to be processed. The abnormal PPG signal is messy, non-periodic, and irregular. Several existing methods such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Deep Neural Network (DNN) and sensor had been used to detect abnormal pattern from PPG signal which can produce high performance and accuracy. However, these methods are higher in complexity or have uncertain repeatability. Therefore, this thesis proposed a method which is rule-based algorithm that is less complex, with quicker and more simple training, reducing the errors while still producing high accuracy. This project’s objectives are to implement rule-based algorithm method for abnormal pattern detection in PPG signals, and to investigate the accuracy and performance of rule-based algorithm in detecting the abnormal pattern. The signal processing, segmentation, feature extraction, training and testing for rule-based algorithm classifier, using wrist PPG during exercise dataset and pulse transmit time dataset, are done in this study to detect the abnormal pattern in PPG signals. The accuracy and coverage of rule for both training and testing process are recorded in order to determine the performance of the method used in this study. The abnormal PPG pattern detection using rule-based algorithm has produced accuracy of 87.30% in training process and 87.18% in testing process with coverage of rule for training and testing, 89.26% and 87.33%. The findings of this project can be further used for application of abnormal pattern in PPG signal such as healthcare and human activity recognition. 2022-06 Undergraduates Project Papers NonPeerReviewed pdf en http://umpir.ump.edu.my/id/eprint/39894/1/EA18084_Hidayah_ThesisV2%20-%20Hidayah%20Mazelan.pdf Siti Nur Hidayah, Mazelan (2022) Abnormal Pattern Detection In Ppg Signals Using Time Series Analysis. College of Engineering, Universiti Malaysia Pahang Al-Sultan Abdullah.
institution Universiti Malaysia Pahang Al-Sultan Abdullah
building UMPSA Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang Al-Sultan Abdullah
content_source UMPSA Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TA Engineering (General). Civil engineering (General)
TK Electrical engineering. Electronics Nuclear engineering
Siti Nur Hidayah, Mazelan
Abnormal Pattern Detection In Ppg Signals Using Time Series Analysis
description The photoplethysmogram (PPG) signal is a data in continuous real-time series. It depicts the peripheral pulse wave that is produced due to heart activity, respiration, and other physiological effects. The time-series signal contains a lot of information which is difficult to be processed. The abnormal PPG signal is messy, non-periodic, and irregular. Several existing methods such as Convolutional Neural Network (CNN), Recurrent Neural Network (RNN), Deep Neural Network (DNN) and sensor had been used to detect abnormal pattern from PPG signal which can produce high performance and accuracy. However, these methods are higher in complexity or have uncertain repeatability. Therefore, this thesis proposed a method which is rule-based algorithm that is less complex, with quicker and more simple training, reducing the errors while still producing high accuracy. This project’s objectives are to implement rule-based algorithm method for abnormal pattern detection in PPG signals, and to investigate the accuracy and performance of rule-based algorithm in detecting the abnormal pattern. The signal processing, segmentation, feature extraction, training and testing for rule-based algorithm classifier, using wrist PPG during exercise dataset and pulse transmit time dataset, are done in this study to detect the abnormal pattern in PPG signals. The accuracy and coverage of rule for both training and testing process are recorded in order to determine the performance of the method used in this study. The abnormal PPG pattern detection using rule-based algorithm has produced accuracy of 87.30% in training process and 87.18% in testing process with coverage of rule for training and testing, 89.26% and 87.33%. The findings of this project can be further used for application of abnormal pattern in PPG signal such as healthcare and human activity recognition.
format Undergraduates Project Papers
author Siti Nur Hidayah, Mazelan
author_facet Siti Nur Hidayah, Mazelan
author_sort Siti Nur Hidayah, Mazelan
title Abnormal Pattern Detection In Ppg Signals Using Time Series Analysis
title_short Abnormal Pattern Detection In Ppg Signals Using Time Series Analysis
title_full Abnormal Pattern Detection In Ppg Signals Using Time Series Analysis
title_fullStr Abnormal Pattern Detection In Ppg Signals Using Time Series Analysis
title_full_unstemmed Abnormal Pattern Detection In Ppg Signals Using Time Series Analysis
title_sort abnormal pattern detection in ppg signals using time series analysis
publishDate 2022
url http://umpir.ump.edu.my/id/eprint/39894/1/EA18084_Hidayah_ThesisV2%20-%20Hidayah%20Mazelan.pdf
http://umpir.ump.edu.my/id/eprint/39894/
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score 13.235362