Table 13.
Comparative study for different IoT methods
| Ref | Description | Accuracy | Sensitivity | Specificity | Year |
|---|---|---|---|---|---|
| [133] | WD to be located on a wrist connected to a Smartphone, which in turn implements MCC services and has access to CC services | – | – | – | 2017 |
| [99] | smart seizure detection framework in the edge of the Internet of Things (IoT) using the discrete wavelet transform, statistical feature extraction, and a naive Bayes (NB) classifier | 98.65 | – | – | 2018 |
| [69] | IoT system is implemented by using ARM7LPC2138, RF modem, accelerometer sensor, sound detection sense, or, temperature sensor. | – | – | – | 2019 |
| [96] | employed two machine learning classifiers, support vector machine (SVM), feedforward neural network (FFNN) and DWT integrated with recent 5G network IoT devices for mobile applications, | 99.6% | 99.7 | 99.3 | 2019 |
| [27] | The EEG recording is measured via a wireless headset, then transmitted to the FPGA, where the deep learning algorithm is embedded | 96.1% | 97.41% | 94.8% | 2020 |
| [95] | The EEG data is segmented and filtered on the low-power device. Then, the time and frequency domain features are calculated. a logistic regression model is implemented. Then transmitted to the gateway to be used for XGBoost classification | 95.8% | 92.0% | 96.1% | 2018 |
| [132] | Two wearable devices, based on ECG and PPG, for long-term monitoring in daily life outside the hospital. | – | – | – | 2017 |
| [73] | Mobile multimedia healthcare framework, electroencephalogram signals from a head-mounted set are recorded and processed using CNN | 99.02% | 92.35% | – | 2018 |
| [14] | EEG signals are captured by a headset of electrodes. The headset acts as an IoT device, e EEG signals are transmitted to a mobile edge computing (MEC) server via a short-range communication protocol such as Wi-Fi or a local area network | 89.13% | 80.16% | 96.67% | 2019 |