Nurrosyidah, Afifah (2019) Analisis Heart Rate Variability (HRV) Dengan Metode Potentials Of Unbalanced Complex Kinetics (PUCK) Dan Multiscale Entropy (MSE) Untuk Identifikasi Aktivitas Sistem Saraf Otonom. Masters thesis, Institut Teknologi Sepuluh Nopember.
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Abstract
Teknologi informasi di bidang kesehatan berperan untuk meningkatkan kualitas penanganan pasien, salah satunya dengan adanya pemantauan kondisi pasien secara real-time melalui analisis heart rate variability (HRV). Terdapat korelasi antara HRV dan autonomic nervous system (ANS), di mana analisis HRV dapat digunakan secara non-invasif untuk memantau aktivitas ANS. Muncul berbagai metode yang bertujuan menemukan representasi yang tepat terhadap kondisi ANS. Akan tetapi, metode tersebut masih belum mampu merepresentasikan sistem ANS secara sempurna. Penelitian ini mengusulkan metode Potentials of Unbalanced Complex Kinetic (PUCK) dan Multiscale Entropy (MSE) untuk menghitung potensial dan kompleksitas pada HRV guna mengetahui hubungannya dengan ANS. Saat ini, belum diketahui dengan pasti hubungan antara PUCK dan MSE dengan kondisi ANS. Maka, penelitian ini bertujuan untuk mengetahui hubungan antara hasil PUCK dan MSE dengan kondisi ANS dengan mengaplikasikan metode tersebut pada data HRV yang ditimbulkan dari berbagai perubahan postur dan aktivitas fisik dari tujuh orang subjek. Hasil dari penelitian ini menunjukkan bahwa nilai SSD1 dan SSD2 pada metode PUCK mampu merepresentasikan aktivitas saraf parasimpatik, baik pada subjek yang melakukan aktivitas fisik, pasien penyakit kardiovaskular, maupun pada prognosis. Metode MSE merepresentasikan aktivitas sistem saraf parasimpatik pada subjek dengan aktivitas fisik dan pasien penyakit kardiovaskular, namun masih belum merepresentasikan aktivitas ANS pada prognosis. Penelitian selanjutnya dibutuhkan guna mengetahui nilai yang spesifik pada metode MSE untuk mendapatkan hasil yang mampu mengukur aktivitas ANS dengan tepat dan akurat.
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Information technology in healthcare plays a role in improving the quality of patient treatment, one of which is through real-time monitoring of a patient's condition using heart rate variability (HRV). There is a correlation between HRV and the autonomic nervous system (ANS), where HRV analysis can be used non-invasively to monitor ANS activity. Various methods have emerged that aim to find an accurate representation of ANS conditions. However, these methods have not yet been able to fully represent the ANS system. This study proposes the Potentials of Unbalanced Complex Kinetic (PUCK) and Multiscale Entropy (MSE) methods to calculate the potential and complexity of HRV in order to determine its relationship with ANS. At present, the exact relationship between PUCK and MSE with ANS conditions is not yet known, possibly due to the absence of appropriate parameter values for the method when applied to HRV data. Therefore, this study aims to determine the relationship between PUCK and MSE results and ANS conditions by applying the method to HRV data derived from various changes in posture and physical activity in seven subjects. MSE analysis may also help detect several cardiovascular diseases; however, information related to the relationship between ANS and heart rate using MSE remains limited, even though the MSE method is widely accepted in clinical analysis. The results of this study show that SSD1 and SSD2 in the PUCK method can represent parasympathetic activity in subjects undergoing physical activity, cardiovascular disease patients, and prognosis assessments. However, the MSE method does not yet represent ANS activity in prognosis assessments, although it does represent parasympathetic nervous system activity in subjects with physical activity and cardiovascular disease patients. Further research is needed to determine the specific parameters in the MSE method in order to obtain results that can accurately and precisely quantify ANS activity.
| Item Type: | Thesis (Masters) |
|---|---|
| Uncontrolled Keywords: | autonomic nervous system, HRV analysis, MSE, PUCK |
| Subjects: | T Technology > T Technology (General) > T57.5 Data Processing |
| Divisions: | Faculty of Information Technology > Information System > 59101-(S2) Master Thesis |
| Depositing User: | Afifah Nurrosyidah |
| Date Deposited: | 05 Aug 2026 07:23 |
| Last Modified: | 05 Aug 2026 07:23 |
| URI: | http://repository.its.ac.id/id/eprint/65302 |
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