This paper presents a new method for the analysis of newborn infant cry to detect hypothyroidism. Hypothyroidism is a condition caused by insufficient production of thyroid hormone by the thyroid gland. The proposed technique is robust as it automatically extracts and analyzes features of infant cry signal. In preprocessing of the signals, separation of voice or unvoiced sections is implemented with automatic segmentation that integrates zero rate crossing and short time energy methods. For feature extraction, the Mel frequency cepstrum coefficient analysis is used to extract main features from the infant cry signal. Then, similarities between the two types of signals and significant information are studied using the F-Ratio analysis. Results show that the F-Ratio analysis was able to discriminate between essential and non-essential features for classification.

No abstract

No abstract

No abstract

<span>The human Autonomic Nervous System (ANS) controls the body’s physiological responses such as heart rate, electrodermal activity, temperature, and pupil diameter. The physiological responses are increased in the presence of a stressing stimuli and this is a typical ANS response. However, in case of children with Autism Spectrum Disorder (ASD), </span><span>they suffer from autonomic dysregulation as reported in past owing to their atypical ANS response. This study investigated the ANS response of children with ASD and compares it with the response of normal children. </span><span>EDA response datasets of 35 children with ASD and 55 normal children were acquired with the help of E4 wristband at a sampling rate of 4Hz. </span><span>The signals were preprocessed to remove artefacts and noise and later compared. Furthermore, an SVM classifier was also used to classify the EDA response signals of normal children and children with ASD. The obtained results highlight that the ANS response of children with ASD is atypical as their EDA response is blunt and shows no significant tonic and phasic changes in EDA levels in the presence of stressing stimuli. In addition to that, an accuracy of 75% was obtained using the LF kernel of SVM classifier. The study further unfolds the hypoactive sympathetic response of children with ASD during a stressing event. Furthermore, this will help in future to anticipate the emotional responses of children with ASD such as anger, happiness, and anxiety.</span>