###### Abstract  
Early-stage detection of cardiac autonomic neuropathy (CAN) is important for better management of the disease and prevents hospitalization. This study has investigated the complex nature of PR, QT, RR, and ST time segments of ECG signals by computing the fractal dimension (FD) of all segments from 20 min ECG recordings of people with different severity of the disease and healthy individuals. The mean computed for each ECG time segment to distinguish between subjects was insufficient for an early diagnosis. Statistical analysis shows that the change of FD in various time segments of ECG throughout the recording was most suitable to assess the steps for severity in symptoms of CAN between the healthy and the subjects with early symptoms of CAN. The complexity of ECG features was evaluated using various classifier models, namely, support vector machine (SVM), naive Bayes, random forest, K-nearest neighbor (KNN), AdaBoost, and neural networks. Performance measures were computed on all models, with a maximum neural network classifier having an accuracy of 96.9%. Feature ranking results show that fractal features have more significance than the time segments of ECG in differentiating the subjects. The results of statistical validation show that all the selected features based on ECG physiology proved to have an evident complexity change between normal and severity stages of CAN. Thus, this work reports the complexity analysis in all the selected time segments of ECG that can be an effective tool for early diagnostics for CAN. 26 April 2022 2 June 20226 June  
Many patients are asymptomatic in the early stages of CAN and only a few patients progress to severe symptoms [8]. Early detection of CAN leads to better health care. The routine clinical assessment for CAN requires an analysis of an electrocardiogram (ECG) [9]. There are morphological changes corresponding to CAN that are evident in heart activity, therefore better understanding of methods for interpreting the ECG is important [10]. ECG is the electrical recording associated with cardiac activity; a single heartbeat corresponds to one normal sinus cycle of ECG that is described by five indenting points: P, Q, R, S, and T. Healthy heart activity should be consistent and regular but not periodic [11], periodicity defined based on RR interval. Heart rate variability (HRV) has been reported to be associated with CAN [12; 13]. A decrease in HRV is a clinical indicator of early CAN (eCAN) [14]. The majority of research on the topic has focused on the variation of the RR intervals [15], while other intervals are also associated with CAN. QT segment indicates the systolic phase of a cardiac cycle, and a prolonged QT interval [12] is an indicator of myocardial ischemia, commonly found in CAN patients. QT interval prolongation marks a sign of definite CAN (dCAN). ST segment is associated with myocardial ischemia and is hence suitable for CAN diagnosis [13]. Depression in the ST segment implies that there are acute variations that indicate the severe CAN (sCAN) in individuals. PR segment indicates a delay between atrial and ventricular activation and is found to be associated with QT changes. Thus, four ECG segments, PR, QT, RR, and ST, have associations with different severity of the disease. However, there are significant inter-beat variations of these features because of which there can be errors. Earlier studies have focused on one of the segments. ECG signals have self-similar patterns that are repetitive, and abnormality in the morphological functioning of the heart changes the repetitive structure of the ECG wave [16]. Identifying the ECG features specific to CAN requires accurate segmentation of the signal, which can be a challenge [17]. Fractal dimension (FD) is a measure of self-similarity [18]. It is a global feature that identifies the signal trend and is thus not disturbed by inter-segment variation. We hypothesize that FD of ECG is associated with CAN and is suitable for early detection of the disease.