Automated detection of depression using wavelet scattering networks.

Today, depression is a common problem that affects many people all over the world. It can impact a person's mood and quality of life unless identified and treated immediately. Due to the hectic and stressful modern life seems to be, depression has become a leading cause of mental health illnesses. Signals from electroencephalograms (EEG) are frequently used to detect depression. It is difficult, time-consuming, and highly skilled to manually detect depression using EEG data analysis. Hence, in the proposed study, an automated depression detection system using EEG signals is proposed. The proposed study uses a clinically available dataset and dataset provided by the Department of Psychiatry at the Government Medical College (GMC) in Kozhikode, Kerala, India which consisted of 15 depressed patients and 15 healthy subjects and a publically available Multi-modal Open Dataset (MODMA) for Mental-disorder Analysis available at UK Data service reshare that consisted of 24 depressed patients and 29 healthy subjects. In this study, we have developed a novel Deep Wavelet Scattering Network (DWSN) for the automated detection of depression EEG signals. The best-performing classifier is then chosen by feeding the features into several machine-learning algorithms. For the clinically available GMC dataset, Medium Neural Network (MNN) achieved the highest accuracy of 99.95% with a Kappa value of 0.999. Using the suggested methods, the precision, recall, and F1-score are all 1. For the MODMA dataset, Wide Neural Network (WNN) achieved the highest accuracy of 99.3% with a Kappa value of 0.987. Using the suggested methods, the precision, recall, and F1-score are all 0.99. In comparison to all current methodologies, the performance of the suggested research is superior. The proposed method can be used to automatically diagnose depression both at home and in clinical settings.

The effect of sperm DNA fragmentation on intracytoplasmic sperm injection outcome.

Our study objective was to assess the effect of various sperm DNA fragmentation levels on clinical intracytoplasmic sperm injection outcome. This retrospective study included 392 patients who underwent ICSI and performed sperm DNA fragmentation testing before the procedure. Based on sperm DNA fragmentation cut-off values, the patients were differentiated into 3 groups as <20%, 20%-30% and >30%. According to the female status, patients were differentiated into favourable group (n = 259) with female age <35 years and anti-Mullerian hormone level ≥7.1 pmol/L; and unfavourable group (n = 133) with female age ≥35 years and anti-Mullerian hormone level ≤7.1 pmol/L. The patient's medical records were reviewed, and patient's demographic, laboratory data including semen analysis, sperm DNA fragmentation determined by means of sperm chromatin dispersion, hormonal profile and data regarding intracytoplasmic sperm injection cycle were collected. This cohort reported that the clinical reproductive outcomes of intracytoplasmic sperm injection showed no statistical significance with increase sperm DNA fragmentation levels. In sperm DNA fragmentation above 30%, favourable females had significantly higher clinical pregnancy rate and live birth rate than unfavourable females, while fertilisation rate and miscarriage rate showed no significance between the subgroups. High sperm DNA fragmentation is linked to poor semen parameters.