pyphysio: A physiological signal processing library for data science approaches in physiology
The lack of open-source tools for physiological signal processing hinders the development of standardized pipelines in physiology. Researchers usually must rely on commercial software that, by implementing black-box algorithms, undermines the control on the analysis and prevents the comparison of th...
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Main Authors: | , , , , |
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Other Authors: | |
Format: | Article |
Language: | English |
Published: |
2019
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Subjects: | |
Online Access: | https://hdl.handle.net/10356/81261 http://hdl.handle.net/10220/50394 |
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Institution: | Nanyang Technological University |
Language: | English |
Summary: | The lack of open-source tools for physiological signal processing hinders the development of standardized pipelines in physiology. Researchers usually must rely on commercial software that, by implementing black-box algorithms, undermines the control on the analysis and prevents the comparison of the results, ultimately affecting the scientific reproducibility. We introduce pyphysio as a step towards a data science approach oriented to compute physiological indicators, in particular of the Autonomic Nervous System activity. pyphysio serves as a basis for machine learning modules and it implements a suite of combinable algorithms for processing of signals from either by wearable or medical-grade quality devices. |
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