PMID- 34250917
OWN - NLM
STAT- MEDLINE
DCOM- 20220331
LR  - 20220401
IS  - 1878-7401 (Electronic)
IS  - 0928-7329 (Linking)
VI  - 30
IP  - 2
DP  - 2022
TI  - Heart Rate Variability measured during rest and after orthostatic challenge to 
      detect autonomic dysfunction in Type 2 Diabetes Mellitus using the Classification 
      and Regression Tree model.
PG  - 361-378
LID - 10.3233/THC-213048 [doi]
AB  - BACKGROUND: According to the World Health Organization, one in ten adults will 
      have Type 2 Diabetes Mellitus (T2DM) in the next few years. Autonomic dysfunction 
      is one of the significant complications of T2DM. Autonomic dysfunction is usually 
      assessed by standard Ewing's test and resting Heart Rate Variability (HRV) 
      indices. OBJECTIVE: Resting HRV has limited use in screening due to its large 
      intra and inter-individual variations. Therefore, a combined approach of resting 
      and orthostatic challenge HRV measurement with a machine learning technique was 
      used in the present study. METHODS: A total of 213 subjects of both genders 
      between 20 to 70 years of age participated in this study from March 2018 to 
      December 2019 at Smt. Kashibai Navale Medical College and General Hospital 
      (SKNMCGH) in Pune, India. The volunteers were categorized according to their 
      glycemic status as control (n= 51 Euglycemic) and T2DM (n= 162). The short-term 
      ECG signal in the resting and after an orthostatic challenge was recorded. The 
      HRV indices were extracted from the ECG signal as per HRV-Taskforce guidelines. 
      RESULTS: We observed a significant difference in time, frequency, and non-linear 
      resting HRV indices between the control and T2DM groups. A blunted autonomic 
      response to an orthostatic challenge quantified by percentage difference was 
      observed in T2DM compared to the control group. HRV patterns during rest and the 
      orthostatic challenge were extracted by various machine learning algorithms. The 
      Classification and Regression Tree (CART) model has shown better performance 
      among all the machine learning algorithms. It has shown an accuracy of 84.04%, 
      the sensitivity of 89.51%, a specificity of 66.67%, with an Area Under Receiver 
      Operating Characteristic Curve (AUC) of 0.78 compared to resting HRV alone with 
      75.12% accuracy, 86.42% sensitivity, 39.22% specificity, with an AUC of 0.63 for 
      differentiating autonomic dysfunction in non-diabetic control and T2DM. 
      CONCLUSION: It was possible to develop a Classification and Regression Tree 
      (CART) model to detect autonomic dysfunction. The technique of percentage 
      difference between resting and orthostatic challenge HRV indicates the blunted 
      autonomic response. The developed CART model can differentiate the autonomic 
      dysfunction using both resting and orthostatic challenge HRV data compared to 
      only resting HRV data in T2DM. Thus, monitoring HRV parameters using the CART 
      model during rest and after orthostatic challenge may be a better alternative to 
      detect autonomic dysfunction in T2DM as against only resting HRV.
FAU - Rathod, Shashikant
AU  - Rathod S
AD  - Department of Instrumentation and Control Engineering, College of Engineering, 
      Pune, India.
FAU - Phadke, Leena
AU  - Phadke L
AD  - Department of Physiology, Smt. Kashibai Navale Medical College and General 
      Hospital, Pune, India.
FAU - Chaskar, Uttam
AU  - Chaskar U
AD  - Department of Instrumentation and Control Engineering, College of Engineering, 
      Pune, India.
FAU - Patil, Chetankumar
AU  - Patil C
AD  - Department of Physiology, Smt. Kashibai Navale Medical College and General 
      Hospital, Pune, India.
LA  - eng
PT  - Journal Article
PL  - Netherlands
TA  - Technol Health Care
JT  - Technology and health care : official journal of the European Society for 
      Engineering and Medicine
JID - 9314590
SB  - IM
MH  - Adult
MH  - *Autonomic Nervous System Diseases
MH  - *Diabetes Mellitus, Type 2/complications
MH  - *Diabetic Neuropathies/diagnosis
MH  - Female
MH  - Heart Rate/physiology
MH  - Humans
MH  - India
MH  - Male
OTO - NOTNLM
OT  - Heart Rate Variability (HRV)
OT  - Type 2 diabetes mellitus (T2DM)
OT  - autonomic dysfunction
OT  - classification and regression tree (CART)
OT  - machine learning
OT  - orthostatic challenge
EDAT- 2021/07/13 06:00
MHDA- 2022/04/01 06:00
CRDT- 2021/07/12 08:58
PHST- 2021/07/13 06:00 [pubmed]
PHST- 2022/04/01 06:00 [medline]
PHST- 2021/07/12 08:58 [entrez]
AID - THC213048 [pii]
AID - 10.3233/THC-213048 [doi]
PST - ppublish
SO  - Technol Health Care. 2022;30(2):361-378. doi: 10.3233/THC-213048.