OBJECTIVE: This paper presents a machine learning-based approach for the automatic classification of regular and irregular capnogram segments.
METHODS: Herein, we proposed four time- and two frequency-domain features experimented with the support vector machine classifier through ten-fold cross-validation. MATLAB simulation was conducted on 100 regular and 100 irregular 15 s capnogram segments. Analysis of variance was performed to investigate the significance of the proposed features. Pearson's correlation was utilized to select the relatively most substantial ones, namely variance and the area under normalized magnitude spectrum. Classification performance, using these features, was evaluated against two feature sets in which either time- or frequency-domain features only were employed.
RESULTS: Results showed a classification accuracy of 86.5%, which outperformed the other cases by an average of 5.5%. The achieved specificity, sensitivity, and precision were 84%, 89% and 86.51%, respectively. The average execution time for feature extraction and classification per segment is only 36 ms.
CONCLUSION: The proposed approach can be integrated with capnography devices for real-time capnogram-based respiratory assessment. However, further research is recommended to enhance the classification performance.
OBJECTIVES: This study aims to develop a highly accurate, portable FMD and to demonstrate real-time monitoring of force applied by health professionals during JMT without altering its execution.
METHODS: The FMD was constructed using the FlexiForce sensor, potential divider, ATmega 328 microcontroller, custom-written software, and liquid crystal display. The calibration, accuracy, and cyclic repeatability of the FMD were tested from 0 to 90 N applied load with a gold standard universal testing machine. For practical demonstration, the FMD was tested for monitoring applied force by a physiotherapist while performing Maitland's grade I to IV over the 6th cervical vertebra among 30 healthy subjects.
RESULTS: The obtained Bland-Altman plot limits agreement for accuracy, and cyclic repeatability was -1.57 N to 1.22 N, and -1.26 N to 1.26 N, respectively with standard deviation and standard error of the mean values of 3.77% and 0.73% and 2.15% and 0.23%, respectively. The test-retest reliability of the FMD tested by the same researcher at an interval of one week showed an excellent intra-class correlation coefficient of r= 1.00. The obtained force readings for grade I to IV among 30 subjects ranged from 10.33 N to 45.24 N.
CONCLUSIONS: Appreciable performance of the developed FMD suggested that it may be useful to monitor force applied by clinicians during JMT among neck pain subjects and is a useful educational tool for academicians to teach mobilization skills.
METHODS: The proposed device measures and displays the FHR on a screen liquid crystal display (LCD). The device consists of hardware that comprises condenser microphone sensor, signal conditioning, microcontroller and LCD, and software that involves the algorithm used for processing the conditioned fetal heart signal prior to FHR display. The device's performance is validated based on analysis of variance (ANOVA) test.
RESULTS: FHR data was recorded from 22 pregnant women during the 17th to 37th week of gestation using the developed device and two standard devices; AngelSounds and Electronic Stethoscope. The results show that F-value (1.5) is less than F𝑐𝑟𝑖𝑡, (3.1) and p-value (p> 0.05). Accordingly, there is no significant difference between the mean readings of the developed and existing devices. Hence, the developed device can be used for monitoring FHR in clinical and home environments.