# Plant Electrical Signal Classification Based on Waveform Similarity
## 5 Discussion  
In the same plant, there is no variation in the parameters of AP--e.g., amplitude, duration--once a critical membrane potential threshold is exceeded. However, there are variations in different plants. Although the parameters of the APs evoked by electrical stimulation were highly variable (range 10-80 mV, duration time from 2 s to 30 s), once a critical membrane potential threshold is exceeded, it was related neither to the intensity nor to the duration of the stimulation pulse, namely the phenomenon is a typical all-or-nothing response. Similar results were reported by others [79]. The results also suggested that our template matching algorithm is potential useful tool to classify these various waveforms of APs. Furthermore, it is worth studying the mechanism of variations in APs among these individuals in the future. In Table 10, we compared four classifiers performance. Here, we assume TP (true positive) as the APs detected, FP (false positive) indicates that classifying the non-AP as AP, TN (true negative) indicates that non-APs were classified as non-AP, FN (false negative) indicates that classifying the APs as non-AP. Then, the sensitivity or true positive rate can be expressed as \(sensitivity=\frac{TP}{TP+FN}\). The specificity or true negative rate can be expressed as \(specificity=\frac{TN}{TN+FP}\). The positive predictive value can be expressed as \(PPV=\frac{TP}{TP+FP}\). The negative predictive value is \(NPV=\frac{TN}{TN+FN}\). It is clear that template matching is the best classifier among them. Although the accuracy of BP neural networks reached 85.18%, its training time is very long and it cannot distinguish the non-AP. Moreover, users have to select proper parameters by comparing classification performance in many tests with difference parameters. SVM is an efficient classifier for its short running time. The results show that SVM cannot recognize non-AP correctly. Users have to select or construct a proper kernel function and try to optimize parameters to get better results. Deep learning is a potential good choice though its accuracy is lowest. All AP were detected, but all non-AP were treated as AP. The dataset may be still small for deep learning in this paper. In addition, DBN can be used to extract key feature vectors from the dataset, then these features can be trained by other classifiers such as SVM. By combining other classifiers, DBN may have better results. In this paper, template matching method is the most reasonable method for higher PPV and NPV. The time costs depend on calculating coefficients which time complexity is O(N). It has generalization capacity with the increase of data, but when the template library gets larger, it will need more time to matching different templates. To get a good accuracy and keep the template number from increasing so fast, it is very important to adjust the two thresholds in template matching when adding and updating the templates. Furthermore, reducing the matching time is a more sophisticated problem, and false templates may be added into the template library, how to keep a standard template library is another important problem. In other application scenarios, there were even composite signals involving both APs and VPs induced by strong damage such as burning. In fact, as a versatile new way, our proposed method is able to extract peaks from process of a VP induced by heat-wounding, for which the data was published in 1998 by [PERSON] and his coauthors [93]. Next, the extracted peaks could be classified