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All Journal Jurnal EECCIS Jurnal VOK@SINDO Jurnal Informatika dan Teknik Elektro Terapan Journal of Development Research Sinkron : Jurnal dan Penelitian Teknik Informatika J I M P - Jurnal Informatika Merdeka Pasuruan Jurnal Pengabdian Masyarakat Bestari (JPMB)
Salnan Ratih Asriningtias, Salnan Ratih
Program Studi Magister Teknik Elektro Fakultas Teknik, Universitas Brawijaya
Humanities Civil Engineering, Building, Construction & Architecture Computer Science & IT Decision Sciences, Operations Research & Management Education Engineering Industrial & Manufacturing Engineering Social Sciences Other

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ECG-Based Heart Rate Variability and KNN Classification for Early Detection of Baby Blues Syndrome in Postpartum Mothers Megawati, Citra Dewi; Asriningtias, salnan Ratih; Bima Romadhon Parada Dian; Teo Pei Kian; Sutawijaya, Bayu; Fransiska, Ratna Diana
Sinkron : jurnal dan penelitian teknik informatika Vol. 9 No. 4 (2025): Articles Research October 2025
Publisher : Politeknik Ganesha Medan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33395/sinkron.v9i4.14956

Abstract

Early detection of baby blues syndrome plays an important role in preventing postpartum emotional disturbances from developing into more serious mental health conditions. This study proposes a simple and non-invasive approach to identify early signs of baby blues in postpartum mothers by analyzing electrocardiogram (ECG) signals using the K-Nearest Neighbor (KNN) algorithm. The ECG data were gathered through wearable sensors and processed to extract heart rate variability (HRV) features such as RMSSD, SDNN, entropy, and energy. These features were then used to train and test a KNN classification model through a five-fold cross-validation process. KNN was chosen because it is easy to implement, does not assume any specific data pattern, and works well with small datasets like those commonly found in clinical settings. Its ability to group data based on similarity makes it suitable for recognizing subtle physiological changes linked to emotional stress. The model reached an accuracy of 87.5%, with strong precision and recall scores, showing its reliability in distinguishing mothers who show early symptoms of baby blues from those who do not. Among all features, RMSSD and SDNN had the highest impact, pointing to reduced parasympathetic activity in affected individuals. These findings suggest that combining HRV analysis with a straightforward machine learning approach like KNN offers a promising, low-cost solution for early emotional screening in maternal care, especially where resources are limited.
Co-Authors Abdullah, Harnan Malik Bima Romadhon Parada Dian Dian Kusumaningtyas, Dian Dwi Utari Surya Eka Ratri Noor Wulandari Eka Ratri Wulandari Elta Sonalitha Erni Yudaningtyas Harry Soekotjo Dachlan I Dewa Made Widia I Dewa Made Widia Irmawati, Dini Kurnia Megawati, Citra Dewi Myro Boyke Persijn Natalia, Deasy Chrisnia Novita Rosyida Pawestri, Titi Ayu Persijn, Myro Boyke Rachmad Andri Atmoko Ratna Diana Fransiska Rosyida, Novita Sovia Rosalin Sutawijaya, Bayu Teo Pei Kian Widia, I Dewa Made Wulandari, Eka Ratri