Smart Healthcare System Design. Группа авторов
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of what would happen in a real situation and designed with a realistic point of view to make sure that the feasibility of constructing such a device with this algorithm would be preserved. In addition, the seizure prediction was empirically defined as seconds to safely provide enough preictal data for all of the seizure cases. This seizure prediction horizon was dynamically defined as each of the training sets were constructed for each case that was real time data tested. For each testing case, the training data consisted of all other seizure cases from the same patient.
All of the algorithm feature sets results described in Table 1.6 is extracted from the signals as the testing sample. Since there are feature sets that compare each of the channels to each other, the emulator is supplied data segments from all of the channels at once for each 3-second segment. The performance analysis of Hybrid Artificial Neural Network with Support Vector Machine (HANNSVM) is compared with the existent methods. Such methods used for analysis are one-class SVM method [12], neural network [13]. The one-class SVM classifiers are then executed with the respective training and testing sets described, and their predictions are given to the decision fusion based module. The neural network algorithm reduction of a new permutation of the training data is done where the training data used is a combination of all the seizure cases from the same patient except for the case being used as testing. neural network algorithm is also applied to the testing sample using the same parameters so that the testing data is properly scaled with the training data. The parameters used for performance analysis are classification accuracy and running time. Accuracy is measured where is perfectly identified count of true positive records and is the absolute count of positive records for infected person category. The combination average value of NIMH patients’ data and MIT-CHB patient data average values are shown in Table 1.6 and Figure 1.10. The performance analysis in Table 1.6 exhibits the enhanced accuracy based on patient EEG features of Hybrid Artificial Neural Network with Support Vector Machine (HANNSVM) over other existent methods. The pictorial representation of performance analysis is shown in Figure 1.10.
Table 1.6 Feature sets that resulted in the highest prediction accuracy for patient dataset.
The combination average value of NIMH type of epilepsy data and MIT-CHB type of epilepsy data average and calculate the total averages. The performance analysis in Table 1.7 exhibits the type of epilepsy based accuracy values of Hybrid Artificial Neural Network with Support Vector Machine (HANNSVM) over other existent methods. The pictorial representation of performance analysis is shown in Figure 1.11.
The research has been tested perfectly and successful results are achieved. These results are successfully uploaded to the cloud using Raspberry Pi. Protocols like SPI have been understood and verified its functionality with practical implementation. The research justifies the terms “Embedded System” and “Internet of Things” as it is integrated the hardware and software serving for dedicated application via internet as a medium for data transmission and storing the information. After verifying the results, it is proved that designed research can be used for real-time environment without any error absolutely. The usage of internet of things technology helped the research to access the web portal globally. Such that by seeing the results respective step is taken to prevent before the health condition of patient is going even worse. The system is able to provide the solutions for the problems faced in real time and perfect achievement is succeeded.
Figure 1.10 Output result accuracy predictions for based on patient EEG data.
Table 1.7 Feature sets that resulted in the highest prediction accuracy for each type of epilepsy.
Figure 1.11 Accuracy predictions for based on type of epilepsy.
1.6 Conclusion
This research presented new hybrid methods and algorithms to enhance the prediction of ictal states. Several experiments were proposed to determine the behavior of a possible preictal state of both NIMH patients’ data and MIT-CHB data. A study of the feature sets that maximized the accuracy was completed. Finally, an emulator was tested with pythonide with single channel to the frontal lobe of the brain and compared three algorithm results in a real-time environment. Since there is usually critical damage causing the seizures, there is an indeterminate number of possible confounding variables. For this reason, the prediction system was designed to learn and predict seizures from within the same patient.
At the time point of each state transition, the appropriate flags are set on the processing unit, which trigger indicators. For a state transition to seizure onset, an indicator should go off and the unit should begin to vibrate. At this point, if it is possible to administer AEDs directly to the brain, it should be done either manually or automatically through an implanted drug reservoir. If the patient then transitions to an ictal state regardless of the treatment, an alarm should sound to warn others and appropriate medical attention should be called upon, automatically. The device should record the EEG data for each seizure that happens and use it later for learning. It should also store some metadata about the seizure along with some vitals such as body temperature and heart rhythm. This type of data could help doctors better understand a patient’s condition as well as the progression and development of the condition over time. In the proposed system of Hybrid Artificial Neural Network with Support Vector Machine (HANNSVM) based classification attain better accuracy in terms of efficiency and time duration.
1.6.1 Future Scope
This proposal, seizure prediction has been reduced to less than 10 variables. Improving the method of optimizing all the necessary variables would make the system work more efficiently and would result in less time with a specialist. Lastly, research must be done on the transition from the ictal state back to the interictal state to see how long after a seizure the EEG returns completely back to normal, allowing the system to learn to reset itself accurately and automatically. These additions would help reduce the number of false positives and make the implementation more robust and reliable.
References
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3. Kumar, T.S.,