The Internet of Medical Things (IoMT) is the amalgamation of medical applications using sensor devices to connect with health-related information. In this scenario, user health data are sensed by the IoMT and transferred to the chief physician through a modern communication system. Even without the physical presence of the patient, the doctor can naturally view his or her health condition to prescribe medication. Thus, transferring and storing the sensitive data must be secured.

Sleep is one of the fundamental daily needs and is significant for brain function. Any sleep disorder can naturally affect general health and cause serious health problems, such as brain stroke, high blood pressure, complicate daily activities, and risk safety. Experts use Polysomnography (PSG) to study sleep disorders. PSG is a collection of signals recorded from various sensors counting the Electroencephalogram (EEG), Electro oculography (EOG), Electromyogram (EMG), Electrocardiogram (ECG), airflow, thoracic and abdominal movements, and oximetry. Rechtschaffen and Kales standardized the sleep stage classification rules depending on EEG modifications and split Non-Rapid Eye Movement (NREM) in sleep is a natural relief of humans and prevents several diseases. Sleep apnea is a sleep disorder in which breathing problem occurs during sleep states. SA affects our health by decreasing the oxygen blood level, eventually leading to other diseases [1]. SA impacts our health, mentally and physically, causing diseases, such as stroke, cardio problems, and diabetes. Scientifically, based upon the international sleep expert’s key observation, roughly one billion people worldwide have obstructive sleep apnea (OSA) [2].

IoMT collects significant health-related data and helps early diagnosis of diseases using machine learning techniques. Persons health data is considered extremely sensitive and confidential to users. Thus, IoMT must ensure user privacy [3] through a federated learning technique. This learning method trains the device to stop sharing the data outside the device [4]. For SA diagnosis, PSG test is needed, which produces electrical energy signals from sensor devices attached to the human body. The process of sleep monitoring people with SA is highly expensive and beds remain limited. Sometimes, a full night or two nights are required for monitoring in a well-equipped sleep laboratory. PSG-recorded values sometimes misguide health professionals, leading to inaccurate diagnosis.

However, monitoring a person’s health condition continuously is extremely difficult. Nonetheless, IoMT makes this possible. Although data transfer is performed remotely, its security and privacy are not assured. Moreover, data are sensitive, and a need for authentication technology persists, given that various attacks can enter the IoMT environment through the internet. Some attackers easily acquire control over the medical device remotely. Malware in IoMT greatly affects its communication and control on the medical device. Moreover, traditional techniques are insufficient to highly secure IoMT communication. Recent attacks by Mirai botnets create distributed denial of service due to insecurity in the Internet of Things (IoT) environment. Hence, a strong security technique to detect attacks in the sensitive IoMT environment is warranted [5–7].

This research contributes security features as follows:

1. Sleep apnea is monitored using health sensors, such as an oximeter, and stress is monitored on the basis of the heart rate. Data are collected and transmitted to fog nodes using the IoMT framework.

2. IoMT uses forward neural network to train the features and uses KNN to classify SA accurately. The classified data is stored in fog nodes using a signature encryption technique.

The rest of the article is structured as follows. Section 2 presents the survey on IoMT. Section 3 demonstrates the proposed architecture and apnea detection using IoMT. Section 4 discusses the result evaluation. Section 5 concludes.

The information or data is connected and communicated through the internet using the IoT. Smart devices of homes, cars, watches, and so on are connected to the internet to transfer real-time data from user to end user. Moreover, IoT produces Wireless Sensor Networks (WSN), smart technologies, cloud network, and so on [8]. Human to machine communication [9] is abruptly transferred to machine-machine communication. Recently the IoT became promising in smart applications, such as the health industry, smart city, and so on. Thus, this survey focused on intelligent monitoring of SA diagnosis continuously. The Internet of Intelligent Things (IoIT) [10–12] blends the artificial intelligence in the healthcare system to analyze the data logically.

The main disadvantages of using the IoT is limited bandwidth processing, less memory, and small structure, which cause security attacks and privacy threats [13–16]. These factors influence high research interest in IoT security. To address IoT security, various cryptographic frameworks and technology are suggested [17–22]. Especially in the medical system, handling patient data is highly sensitive for the IoT [23,24]. Central cloud server requires high security structure for providing data authentication.

Some studies explored the adaptive architecture for IoMT architecture [25]. This work provides high security for health dataset using public and private key structures. However, the security of the system is not highly adaptive. Thus, this work explores whether the hybrid security system using a cyber security algorithm for the IoMT is implemented [26]. Streaming data is transmitted via nodes using high authentication [27] to secure the IoMT real-time data in networks. Previous research discussed the security of IoMT with different techniques [28].

This study discussed several SA surveys. The novel recurrent neural architecture is used to extract the apnea features from input dataset [29]. Machine learning classifiers, such as the support vector machine (SVM) [30], threshold-based detectors [31], and regression trees with Adaboost are used to classify SA from the input feature set. The health-based smart contracts [32,33] are used for IoMT to transfer health data [34–38].

Although SA syndrome is a common disease, patient diagnosis is difficult. Thus, smart monitoring of the sleeping habit of patients is presented. It monitors the variations in stress and observes the patient’s heart rate and the blood oxygen level while asleep. The architecture of SA syndrome analysis is presented in Fig. 1. The sensor transmits the values to the data analysis section. Here, SA is analyzed on the basis of the input SpO2 value. The forward neural network and KNN classifier is used to detect SA in patients.

Independent analysis of SA is a breathing disorder that occurs during a person’s sleep state. In this work, FCNN-KNN+ASE uses two public datasets of Sleep-EDF-2013 and Sleep-EDF-2018. For evaluation, a 20-fold cross validation was applied to all 250 PSG data values of the dataset. The sleeping stages of R & K standard of W, N1, N2, N3, N4, and REM are used to evaluate FCNN-KNN+ASE. The parametric measures are given below.

(3) accuracy=TP+TNTP+TN+FP+FN

(4) precision=TPTP+FP

(5) recall=TPTP+FN

(6) F1−Score=2∗precision∗recallprecision+recall

(7) Kappa=accuracy−Pe1−Pe

(8) MF1=∑i=110F1−scorei5

(9) MAE=1N∑i=1n⁡|∝i−∝i′|

(10) RMSE=1n∑i=1n⁡|∝i−∝i′|

where TP-True Positive, TN-True Negative, FP-False Positive, FN-False Negative, and Pe is Hypothetical probability. Tabs. 2 and 3 shows that confusion matrix of Sleep-EDF-2013 dataset in FCNN-KNN+ASE using 20-fold cross-validation.

Tab. 4 shows the comparison of our proposed work with other machine learning algorithms in the parametric metric measures of kappa and MF1 score values using two different datasets of Sleep-EDF-2013 and Sleep-EDF-2018.

Tab. 5 unveils that the evaluation of FCNN-KNN+ASE was compared with FCNN-ASE and KNN-ASE. Our proposed work obtained better value in kappa of 0.7 in the Sleep-EDF-2013 dataset and obtained a kappa value of 0.8 in the Sleep-EDF-2018 dataset. Similarly, for the MF1 score value of the Sleep-EDF-2013 dataset obtained 81.37% and the Sleep-EDF-2018 dataset obtained 84.65% in our proposed work, FCNN-KNN+ASE. Tabs. 6 and 7 show the calculating error rate of the Sleep-EDF-2013 and Sleep-EDF-2018 datasets.

Tabs. 6 and 7 present tthat the error rate for our proposed work produces minimum error rate using two different datasets. Tabs. 8 and 9 unveil that cost of communication and storage cost in bits of different algorithm with two different datasets are given.

Tab. 8 demonstrates that our proposed technique obtains minimum communication cost and storage cost compared with other techniques.

Tab. 9 presents that our proposed technique obtains minimum communication cost and storage cost compared with other techniques. Tabs. 9 and 10 reveal that time analysis for encryption of PSG information with various file sizes and stored it in fog-node.

Tab. 10 shows that our proposed work obtained minimum time analysis for encryption using the Sleep-EDF-2013 dataset and the PSG information, which are collected from SoP2 and HRV sensor devices for diagnosis of SAS.

Tab. 10 demonstrates that our proposed work obtained minimum time analysis for encryption using the Sleep-EDF-2018 dataset and the PSG information, which are collected from SoP2 and HRV sensor devices for SAS diagnosis.

Our proposed work obtained minimum time analysis for decryption using the Sleep-EDF-2018 dataset and the PSG information, which are collected from SoP2 and HRV sensor devices for SAS diagnosis. Fig. 6 shows the average accuracy rate for FCNN-KNN+ASE in the two datasets.

Fig. 6 presents that our proposed work gives high accuracy rate compared with other algorithms. Fig. 7 shows the computation time of our proposed work.

The figure shows that the computation of our proposed work obtained minimum time. It produces minimum execution time and minimum error rate.

This work provides reference for SA diagnosis using a fog computing-based IoMT in machine learning algorithms. It uses electrical energy signals that are carefully collected from SpO2 and HRV sensor devices and collected data stored in fog computing network. Thereafter, we implement a complex ASE algorithm for encryption and decryption for PSG values, which cannot be accessed by unauthorized users. This work uses two different public datasets of Sleep-EDF-2013and Sleep-EDF-2018. The advantages of the proposed work, FCNN-KNN +ASE, are better security, faster access, minimal time execution, higher accuracy, more flexible, and more reliable compared with other existing algorithms. The overall accuracy performance of this work obtained 92.13% in the Sleep-EDF-2013 dataset and 93.32% in the Sleep-EDF-2018 dataset. Finally, our proposed work analysis provides effective monitoring for diagnosing people with the SA based on PSG values. Future work is upgraded by using various machine learning algorithms with edge computing. Energy efficiency must be calculated in future work to widely enhance fog computing the IoMT.