Signal and Noise Classification in Bio-Signals via unsupervised Machine Learning
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arXiv
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866911309185941504 |
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| author | Paudel, Sansrit |
| author_facet | Paudel, Sansrit |
| contents | Real-world biosignal data is frequently corrupted by various types of noise, such as motion artifacts, and baseline wander. Although digital signal processing techniques exist to process such signals; however, heavily degraded signals cannot be recovered. In this study, we aim to classify two things: first, a binary classification of noisy and clean biosignals, and next, to categorize various kinds of noise such as motion artifacts, sensor failure, etc. We implemented K-means clustering, and our results indicate that the algorithm can most reliably group clean segments from noisy ones, particularly strong performance in identifying clean data compared to various categories of noise. This approach enables the selection of only high-quality bio-signal segments and provides accurate results for feature engineering that may enhance the precision of machine learning models trained on biosignals. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2512_07851 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Signal and Noise Classification in Bio-Signals via unsupervised Machine Learning Paudel, Sansrit Signal Processing Systems and Control Real-world biosignal data is frequently corrupted by various types of noise, such as motion artifacts, and baseline wander. Although digital signal processing techniques exist to process such signals; however, heavily degraded signals cannot be recovered. In this study, we aim to classify two things: first, a binary classification of noisy and clean biosignals, and next, to categorize various kinds of noise such as motion artifacts, sensor failure, etc. We implemented K-means clustering, and our results indicate that the algorithm can most reliably group clean segments from noisy ones, particularly strong performance in identifying clean data compared to various categories of noise. This approach enables the selection of only high-quality bio-signal segments and provides accurate results for feature engineering that may enhance the precision of machine learning models trained on biosignals. |
| title | Signal and Noise Classification in Bio-Signals via unsupervised Machine Learning |
| topic | Signal Processing Systems and Control |
| url | https://arxiv.org/abs/2512.07851 |