1.2 Brainwaves frequency characteristics.
With Neurosky ease MindWave Mobile headset and neuro feedback software sensor measures the mind’s electrical action and exchanges the information [21].
In order to make this system accessible to epileptic patients, a small device with this algorithm could be implemented. Six probes, three on the epileptogenic focus, and three on the opposite lobe, would have to be installed on the patient which would input the signals into the processing unit, possibly by a wireless protocol such as bluetooth or WiFi. The processing unit would have to be attached to the body in a discrete manner, such as a belt or something that can be worn at all times [26, 27].
1.4.2.3 Raspberry pi
Now attach the Arduino board in raspberry pi by pressing the ls/dev/tty in command terminal of raspberry pi. We will get a list of devices available. Paste this /dev/ttyACM0 in the code. The values from the Arduino go to Raspberry Pi. These values are send to the cloud To see the uploaded data go to the webpage “health monitoring system website we created” and login into it, you will see the particular details as shown in Figure 1.7 below.
Figure 1.7 Home pages for EEG signal design.
1.4.2.4 Working
We are utilizing Arduino for mix of sensors i.e., Temperature sensor LM35, Pulse sensor, and EEG sensor. Raspberry Pi is incredible instrument for installed designs yet it needs ADC. One more downside is all its IOs are 3.3V level. On the opposite side Arduino is great at detecting the physical world utilizing sensors. To get advantages of both the frameworks one may need to interface them. EEG sensor is associated with Arduino utilizing Bluetooth module HC-05. Here HC-05 go about as ace and EEG sensor as slave [25]. Its fills in as TTL Master/Slave UART convention correspondence. Outlined by Full fastest Bluetooth task with full piconet bolster. It enables us to accomplish the business’ largest amounts of affectability, precision, with least power utilization [28].
Here we are using cloud of smart bridge to store the data. The data which is collected from the sensors is send to the cloud of domain smart bridge and sub domain health monitoring system through API. The patient can view his health details after logging-in. In this research we are using pulse sensor to know the patient heartbeat, LM35 to know his body temperature and EEG sensors to know his brain signals. So after login he will get a display of readings in tabular form as shown in the figure. In this research, we are using mindwave headset which works on EEG technology. This sensor consists of one main sensor and one reference electrode. This research can be implemented in future by making more sophisticated by expanding the sensors used to read the brain waves. The main working of mindwave mobile headset goes in ThinkGear ASIC module chip. In this research, we are using TGAM chip in the sensor [22].
The EEG Sensor (values of Attention, Meditation), for calculation Range of 1–100 was taken
• Range from 40 to 60 is considered “neutral”.
• Range from 60 to 80 is slightly high, and interpreted as higher over normal.
• Range from 80 to 100 are considered “high”, that mean it is strong indication levels Severe levels.
1.4.3 Cloud Feature Extraction
The most main role in creating an EEG signal classification system is generating mathematical representations and reductions of the input data which allow the input signal to be properly differentiated into its respective classes. These mathematical representations of the signal are, in a sense, a mapping of a multidimensional space (the input signal) into a space of fewer dimensions. This dimensional reduction is known as “feature extraction”. Ultimately, the extracted feature set should preserve only the most important information from the original signal [23].
Table 1.3 EEG signal mathematical transform with feature.
| Set | Mathematical transform | Feature number |
| 1 | Linear predictive codes taps | 1–5 |
| 2 | Fast Fourier transform statics | 6–12 |
| 3 | Mel frequency cepstral coefficients | 13–22 |
| 4 | Log (FFT) analysis | 23–28 |
| 5 | Phase shift correlation | 29–36 |
| 6 | Hilbert transform statics | 37–44 |
| 7 | Wavelet decomposition | 45–55 |
| 8 | 1st, 2nd, 3rd derivatives | 56–62 |
| 9 | 1st, 2nd, 3rd derivatives | 63–67 |
| 10 | Auto regressive parameters | 68–72 |
Table 1.3 above describes feature classification for EEG signal. First, a feature set optimization algorithm is presented which is used to do a feature set study to reveal the mathematical transforms that are most useful in predicting the preictal state. After this, a set of algorithms are given that became the framework of the seizure on set prediction system described.
1.4.4 Feature Optimization
In order to find the features with the most potential, an algorithm was implemented to approximate individual feature strength with respect to every other feature [30]. The strength of a feature was determined by the accuracy with which the preictal state was classified as an average of several classifications. Similar to Cross-Validation by Elimination HANNSVM algorithm repartitions the feature set, performs a set of classifications, finds the best feature sets to drop, and then adjusts the feature space to only contain features that improve the accuracy.
1 1. Evaluate the accuracy of the classification using all N feature sets.
