Hippotherapy, such as horseback riding, com-bines gait, balance, motor behavior, pelvic movements, muscle symmetry, emotional, and psychosocial parameters (1-2). Recently, horse-back riding exercises have been found to be an effective therapy for patients with cerebral palsy and autism spectrum disorders (3-4). . . .

Background & Aim: Iatrogenic paraplegia during spine surgery is a devastating complication. Preventing such complications is important. (IOM) serves to help such patients/surgeons with successful surgeries and better post-op outcome using the IOM modalities.Methods & Materials/Patients: A retrospective data review was done on 153 consecutive patients. Somatosensory- Evoked-Potentials (SSEPs), Transcranial-Motor-Evoked-Potentials (TcMEPs), ELECTROMYOGRAMS (EMGs) & Triggered-ELECTROMYOGRAMS (TEMGs/screw stimulation) were studied.Results: 153 patients (n=104 females, n=49 males) with average age of 17 years and cob-angle ranged between 40-140 degrees were studied. TcMEP changes were noticed in n=56, 36.6%.TcMEP change resulting from surgical maneuvers n=43, 28.10%. TcMEP changes resulted from direct injury to the cord n=16, 10.45% & mechanical intervention (decompression/hemivertebrectomy/correction) n=15, 9.80%. Mal-positioned screws n=12, 7.83%. Within this group n=9, 5.8% presented with both SSEP and TcMEP changes. Changes in TcMEP, due to anesthesia n=10, 6.53%. EMG irritation lasting>15 seconds in lumbar roots, n=14, 9.15%. Within this group, TcMEP drop noticed in 4 patients suggesting 25% predictability were observed. TEMG changes in n=81, 52.9%, where in n=42, 27.4% screws were<6mA.TcMEP change, n=12, 7.83%, all fell under this group of screws below 6mA.3018 screws were placed in 153 patients with 19.73 screws/ patient.89 scre ws, 2.94% fell under dangerous/severe category.Conclusion: Our data showed that enhanced safety with comprehensive IOM in deformity surgeries, hence IOM was extremely important during such procedures.

Background and Objective: Obstructive sleep apnea (OSA) is among the critical sleep disorders, and researchers have been investigating its novel diagnostic methods. Polysomnography signals' complexity, difficult visual interpretation, and the need for an efficient algorithm based on simpler signals have made the study of sleep apnea a compelling issue. In this study, the accuracy of chin electromyogram in the diagnosis of OSA was evaluated. Materials and Methods: The amplitude variation and power spectral density (PSD) of chin ELECTROMYOGRAMS of 100 patients during apnea and before-after apnea occurrences (non-apnea) periods were compared after complete processing of the raw signal. Two-dimensional (2D) spectrograms related to the specified periods were extracted and fed into the residual neural network (ResNet). The network performance was reported by model evaluation parameters. Results: The results showed that OSA event influences the patient's chin muscle and increases the amplitude variances and power spectrum of the chin electromyogram. The ResNet-50 deep model classified the dataset of this sleep disorder with about 97% accuracy, which was higher than previous studies in this field. Conclusion: Chin electromyogram can be introduced as a practical and useful biosignal for accurate OSA diagnosis with a deep classifier without the need for current specialized equipment and multiple vital signals.