Materials and methods: Eighty-four patients were randomly divided into two groups receiving either study drug infusion. Anxiety
score, level of sedation using the Bispectral Index and Observer’s Assessment of Alertness and Sedation, hemodynamic stability, and
overall patient’s feedback on anxiolysis were assessed.
Results: Both groups showed a significant drop in mean anxiety score at 10 and 30 min after starting surgery. Difference in median
anxiety scores showed a significant reduction in anxiety score at the end of the surgery in the dexmedetomidine group compared to the
propofol group. Dexmedetomidine and propofol showed a significant drop in mean arterial pressure in the first 30 min and first 10 min
respectively. Both drugs demonstrated a significant drop in heart rate in the first 20 min from baseline after starting the drug infusion.
Patients in the dexmedetomidine group (76.20%) expressed statistically excellent feedback on anxiolysis compared to patients in the
propofol group (45.20%).
Conclusion: Dexmedetomidine infusion was found to significantly reduce anxiety levels at the end of surgery compared to propofol
during regional anesthesia.
METHODS: The investigated dataset was obtained via long-term measurements in retirement homes and intensive care units (ICU). Data were measured unobtrusively using a measuring pad equipped with piezoceramic sensors. The proposed approach focused on the processing methods of the measured ballistocardiographic signals, Cartan curvature (CC), and Euclidean arc length (EAL).
RESULTS: For analysis, 218,979 normal and 216,259 aberrant 2-second samples were collected and classified using a convolutional neural network. Experiments using cross-validation with expert threshold and data length revealed the accuracy, sensitivity, and specificity of the proposed method to be 86.51 CONCLUSIONS: The proposed method provides a unique approach for an early detection of health concerns in an unobtrusive manner. In addition, the suitability of EAL over the CC was determined.
METHODS: Continuous raw PPG waveforms were blindly allocated into segments with an equal length (5s) without leveraging any pulse location information and were normalized with Z-score normalization methods. A 1-D-CNN was designed to automatically learn the intrinsic features of the PPG waveform, and perform the required classification. Several training hyperparameters (initial learning rate and gradient threshold) were varied to investigate the effect of these parameters on the performance of the network. Subsequently, this proposed network was trained and validated with 30 subjects, and then tested with eight subjects, with our local dataset. Moreover, two independent datasets downloaded from the PhysioNet MIMIC II database were used to evaluate the robustness of the proposed network.
RESULTS: A 13 layer 1-D-CNN model was designed. Within our local study dataset evaluation, the proposed network achieved a testing accuracy of 94.9%. The classification accuracy of two independent datasets also achieved satisfactory accuracy of 93.8% and 86.7% respectively. Our model achieved a comparable performance with most reported works, with the potential to show good generalization as the proposed network was evaluated with multiple cohorts (overall accuracy of 94.5%).
CONCLUSION: This paper demonstrated the feasibility and effectiveness of applying blind signal processing and deep learning techniques to PPG motion artifact detection, whereby manual feature thresholding was avoided and yet a high generalization ability was achieved.