Identification of apnea during respiratory monitoring using support vector machine classifier: a pilot study

Application of artificial intelligence in the diagnosis of sleep apnea

STUDY OBJECTIVES

Machine learning (ML) models have been employed in the setting of sleep disorders. This review aims to summarize the existing data about the role of ML techniques in the diagnosis, classification, and treatment of sleep-related breathing disorders.

METHODS

A systematic search in Medline, EMBASE, and Cochrane databases through January 2022 was performed.

RESULTS

Our search strategy revealed 132 studies that were included in the systematic review. Existing data show that ML models have been successfully used for diagnostic purposes. Specifically, ML models showed good performance in diagnosing sleep apnea using easily obtained features from the electrocardiogram, pulse oximetry, and sound signals. Similarly, ML showed good performance for the classification of sleep apnea into obstructive and central categories, as well as predicting apnea severity. Existing data show promising results for the ML-based guided treatment of sleep apnea. Specifically, the prediction of outcomes following surgical treatment and optimization of continuous positive airway pressure therapy can be guided by ML models.

CONCLUSIONS

The adoption and implementation of ML in the field of sleep-related breathing disorders is promising. Advancements in wearable sensor technology and ML models can help clinicians predict, diagnose, and classify sleep apnea more accurately and efficiently.

CITATION

Bazoukis G, Bollepalli SC, Chung CT, et al. Application of artificial intelligence in the diagnosis of sleep apnea. J Clin Sleep Med. 2023;19(7):1337-1363.

Human respiration monitoring using infrared thermography and artificial intelligence

The respiration rate (RR) is the most vital parameter used for the determination of human health. The most widely adopted techniques, used to monitor the RR are contact in nature and face many drawbacks. This paper reports the use of Infrared Thermography, in reliably monitoring the RR in a contact-less and non-invasive way. A thermal camera is used to monitor the variation in nasal temperature during respiration continuously. Further, the nostrils (region of interest) are tracked during head motion and object occlusion, by implementing a computer vision algorithm that makes use of 'Histogram of oriented gradients' and 'Support vector machine' (SVM). The signal to noise ratio (SNR) of the acquired breathing signals is very low; hence they are subjected to appropriate filtering methods. The filters are compared depending on the performance metrics such as SNR and Mean square error. The breaths per minute are obtained without any manual intervention by implementing the 'Breath detection algorithm' (BDA). This algorithm is implemented on 150 breathing signals and its performance is determined by computing the parameters such as Precision, Sensitivity, Spurious cycle rate, and Missed cycle rate values, obtained as 98.6%, 97.2%, 1.4%, and 2.8% respectively. The parameters obtained from the BDA are fed to the k-Nearest Neighbour (k-NN) and SVM classifiers, that determine whether the human volunteers have abnormal or normal respiration, or have Bradypnea (slow breathing), or Tachypnea (fast breathing). The Validation accuracies obtained are 96.25% and 99.5% with Training accuracies 97.75% and 99.4% for SVM and k-NN classifiers respectively. The Testing accuracies of the completely built SVM and k-NN classifiers are 96% and 99%, respectively. The various performance metrics like Sensitivity, Specificity, Precision, G-mean and F-measure are calculated as well, for every class, for both the classifiers. Finally, the Standard deviation values of the SVM and k-NN classifiers are computed and are obtained as 0.022 and 0.007, respectively. It is observed that the k-NN classifier shows a better performance compared to the SVM classifier. The pattern between the data points fed to the classifiers is viewed by making use of the t-Stochastic Neighbor Embedding algorithm. It is noticed from these plots that the separation between the data points belonging to different classes, improves and shows minimal overlap by increasing the perplexity value and number of iterations.