AUTHOR=Shih Chi-Huang , Chou Pai-Chien , Chen Jin-Hua , Chou Ting-Ling , Lai Jun-Hung , Lu Chi-Yu , Huang Tsai-Wei 

TITLE=Cancer-related fatigue classification based on heart rate variability signals from wearables

JOURNAL=Frontiers in Medicine

VOLUME=Volume 10 - 2023

YEAR=2023

URL=https://www.frontiersin.org/journals/medicine/articles/10.3389/fmed.2023.1103979

DOI=10.3389/fmed.2023.1103979

ISSN=2296-858X

ABSTRACT=Background: Cancer-related fatigue (CRF) is the most distressing side effect in cancer patients and even affects the survival rate. However, most patients do not report their fatigue level. We aimed to develop an objective CRF assessment method based on heart rate variability (HRV). 
Methods: In this study, we enrolled patients with lung cancer who received chemotherapy or target therapy. The patients wore wearable devices with photoplethysmography that regularly recorded HRV parameters for 7 consecutive days and completed the Brief Fatigue Inventory (BFI) questionnaire. The collected parameters were divided into the active and sleep phase parameters to allow tracking of fatigue variation. Statistical analysis was used to identify correlations between fatigue scores and HRV parameters. 
Findings: We enrolled 60 patients with lung cancer.  HRV parameters including the LF / HF ratio and the LF / HF disorder ratio in the active phase and the sleep phase were extracted. A linear classifier with HRV-based cutoff points achieved correct classification rates of 73% and 88% for mild and moderate fatigue, respectively. 
Conclusion: Fatigue was effectively identified, and the data was effectively classified using a 24-hour HRV device. This objective fatigue monitoring method may enable clinicians to effectively handle fatigue problems.