Epileptic Seizure Classification ML Algorithms – In direction of Knowledge Science


Knowledge Exploration

The dataset accommodates a hashed affected person ID column, 178 EEG readings over one second, and a Y output variable describing the standing of the affected person at that second. When a affected person is having a seizure, y is denoted as 1 whereas all different numbers are different statuses we aren’t all for. So once we flip our Y variable right into a binary variable, this downside turns into a binary classification downside.

We can even select to drop the primary column because the affected person id is hashed, and there’s no method for us to make use of that. We use the next code to do all of that.

The following step is to calculate the prevalence fee, and it’s outlined because the proportion of the samples which are constructive in school; in different phrases, in our dataset, it’s the proportion of sufferers which are having a seizure.

Our prevalence fee is 20%. That is helpful to know in terms of balancing courses and evaluating our mannequin utilizing the ‘raise’ metric.


Knowledge Processing and Constructing Coaching/Validation/Check Units

There isn’t any function engineering to be carried out right here since all of our options are numerical values of EEG readings; there isn’t a processing wanted to dump our dataset into our machine studying mannequin.

It’s good observe to separate the predictor and response variables from the dataset.

cols_input are our predictor variables & OUTPUT_LABEL is our response variable

Now it’s time to separate our dataset into coaching, validation, and testing units! How thrilling! Normally, validation and testing units are of the identical dimension, and the coaching units sometimes vary from 50% to 90% of the first dataset, relying on the variety of samples that the dataset has. The extra samples a dataset has, the extra samples we will afford to dump into our coaching set.

Step one is to shuffle our dataset to be sure that there isn’t some order related to our samples.

Then, the chosen cut up is 70/15/15, so lets cut up our dataset that method. We are going to choose first to separate our validation and take a look at units aside from our coaching set, it’s because we would like our validation and testing units to have related distributions.

We are able to then examine the prevalence in every set to ensure they’re roughly the identical, so round 20%.

Subsequent, we wish to stability our dataset to keep away from making a mannequin the place it incorrectly classifies samples as belonging to the bulk class; in our case, it will be sufferers not having a seizure. That is known as the accuracy paradox, for instance when the accuracy of our mannequin tells us that now we have an 80% accuracy, it should solely be reflecting the underlying class distribution if the courses are unbalanced. Since our mannequin sees that almost all of our samples aren’t having a seizure, one of the best factor to attain a excessive accuracy rating is to categorise samples as not having seizures no matter what we ask it to foretell. There are two simple and beginner-friendly methods we may help fight this downside. Sub-sampling and over-sampling. We are able to sub-sample the extra dominant class by decreasing the variety of samples belonging to the extra dominant class, or we will over-sample by pasting the identical samples of the minority class a number of occasions till each courses are equal in quantity. We are going to select to make use of sub-sampling on this challenge.

We then save the train_all , practice , legitimate , and take a look at units as .csv information. Earlier than transferring onto importing sklearn and constructing our first mannequin, we have to scale our variables for a few of our fashions to work. Since we will probably be constructing 9 totally different classification fashions, we should always scale our variables with the StandardScaler .

We dump our scaler as a .csv file for fast entry if we wish to use it in different python notebooks

Classification Fashions

Let’s set it up, so we will print all of our mannequin metrics with one perform print_report .

And since we’ve balanced our information, let’s set out threshold at 0.5. The edge is used to find out whether or not a pattern will get labeled as constructive or unfavourable. It is because our mannequin returns the proportion likelihood of a pattern belonging to the constructive class, so it gained’t be a binary classification with out setting a threshold. If the proportion returned for the pattern is increased than our threshold, then will probably be labeled as a constructive pattern, and so on.

Classification Fashions

We are going to cowl the next fashions:

  • Ok Nearest Neighbors
  • Logistic Regression
  • Stochastic Gradient Descent
  • Naive Bayes
  • Choice Bushes
  • Random Forest
  • Excessive Random Forest (ExtraTrees)
  • Gradient Boosting
  • Excessive Gradient Boosting (XGBoost)

We are going to use baseline default arguments for all fashions, then select the mannequin with the best validation rating to carry out hyperparameter tuning.

Ok Nearest Neighbors (KNN)

KNN is among the first fashions that individuals study in terms of scikitlearn ‘s, classification fashions. The mannequin classifies the pattern based mostly on the okay samples which are closest to it. For instance, if okay = 3, and all three of the closest samples are of the constructive class, then the pattern can be labeled as class 1. If two out of the three nearest samples are of the constructive class, then the pattern would have a 66% chance to be labeled as constructive.

We get a fairly excessive coaching Space Beneath the Curve (AUC) Receiver Operator Curve (ROC), and a excessive validation AUC as effectively. This metric is used to measure the efficiency of classification fashions. AUC tells us how a lot the mannequin is able to distinguishing between courses, the upper the AUC, the higher the mannequin is at distinguishing between courses. If the AUC is 0.5, then you definitely would possibly as effectively guess on the samples.

Logistic Regression

Logistic regression is a sort of generalized linear mannequin, that are a generalization of the ideas and talents of standard linear fashions.

In logistic regression, the mannequin predicts if one thing is true or false, slightly than predicting one thing steady. The mannequin matches a linear resolution boundary for each courses, then is handed by a sigmoid perform to rework from the log of odds to the chance that the pattern belongs to the constructive class. As a result of the mannequin tries to seek out one of the best separation between the constructive class and unfavourable class, this mannequin performs effectively when the info separation is noticeable. This is among the fashions that require all options be scaled, and that the dependent variable is dichotomous.

Stochastic Gradient Descent

Gradient descent is an algorithm that minimizes many loss capabilities throughout many various fashions, comparable to linear regression, logistic regression, and clustering fashions. It’s much like logistic regression, the place gradient descent is used to optimize the linear perform. The distinction is that stochastic gradient descent permits mini-batch studying, the place the mannequin makes use of a number of samples to take a single step as an alternative of the entire dataset. It’s particularly helpful the place there are redundancies within the information, normally seen by clustering. SGD is due to this fact a lot quicker than logistic regression.

Naive Bayes

The naive Bayes classifier makes use of the Bayes theorem to carry out classification. It assumes that if all options aren’t associated to one another, then the chance of seeing the options collectively are simply the product of the chance of every function taking place. It finds the chance of the pattern being labeled as constructive, given all of the totally different mixtures of options. The mannequin is commonly flawed as a result of the “naive” a part of the mannequin assumes all options are impartial, and that’s not the case more often than not.

Choice Bushes

A call tree is a mannequin the place it runs a pattern down a number of “questions” to find out its class. The classifying algorithm works by repetitively separating information into sub-regions of the identical class and the tree ends when the algorithm has divided all samples into classes which are pure, or by assembly some standards of the classifier attributes.

Choice bushes are weak learners, and by that, I imply they aren’t significantly correct, they usually usually solely do a bit higher than randomly guessing. Additionally they nearly all the time overfit the coaching information.

Random Forest

Since resolution bushes are prone to overfit, the random forest was created to cut back that. Many resolution bushes make up a random forest mannequin. A random forest consists of bootstrapping the dataset and utilizing a random subset of options for every resolution tree to cut back the correlation of every tree, therefore decreasing the chance of overfitting. We are able to measure how good a random forest is through the use of the “out-of-bag” information that weren’t used for any bushes to check the mannequin. Random forest can be nearly all the time most well-liked over a choice tree because the mannequin has a decrease variance; therefore, the mannequin can generalize higher.

Extraordinarily Randomized Bushes

The ExtraTrees Classifier is much like Random Forest besides:

  • When selecting a variable on the cut up, samples are drawn from the complete coaching set slightly than bootstrapping samples
  • Node splits are chosen at random, as an alternative of being specified like in Random Forest

This makes the ExtraTrees Classifier much less vulnerable to overfit, and it may possibly usually produce a extra generalized mannequin than Random Forest.

Gradient Boosting

Gradient boosting is one other mannequin that combats the overfitting of resolution bushes. Nevertheless, there are some variations between GB and RF. Gradient boosting builds shorter bushes, one by one, and every new tree reduces the error the earlier tree has made. The error is named the pseudo-residual. Gradient boosting is quicker than a random forest, and are helpful in a number of real-world functions. Nevertheless, gradient boosting doesn’t do this effectively when your dataset accommodates noisy information.

Excessive Gradient Boosting

XGBoost is much like gradient boosting besides

  • Bushes have a various variety of terminal nodes
  • Leaf weights of the bushes which are calculated with much less proof are shrunk extra closely
  • Newton Boosting offers a direct path to the minima than gradient descent
  • Additional randomization parameter is used to cut back the correlation between bushes
  • Makes use of a extra regularized mannequin to regulate over-fitting since commonplace GBM has no regularization, which provides it higher efficiency over GBM.
  • XGB implements parallel processing and is way quicker than GBM.

Mannequin Choice and Validation

The following step is to visualise the efficiency of all of our fashions in a single graph; it makes it simpler to choose which one we wish to tune. The metric I selected to guage my fashions is the AUC curve. You possibly can select any metric you wish to optimize for, comparable to accuracy or raise, nonetheless, the AUC isn’t affected by the brink you select, so it’s a metric that most individuals use to guage their fashions.

Seven of the 9 fashions have a really excessive efficiency, and that is most definitely as a result of excessive variations in EEG readings between a affected person having a seizure and never having one. The choice tree appears to be like prefer it overfitted as anticipated, discover the hole between the coaching AUC and the validation AUC.

I’m going to choose XGBoost and ExtraTrees classifier as the 2 fashions to tune.

Studying Curves

Studying curves are a method for us to visualise the bias-variance tradeoff in our fashions. We make use of the educational curve code from scikit-learn however plot the AUC as an alternative since that’s the metric we selected to guage our fashions with.

Each the coaching and CV curves are excessive, so this indicators each low variance and low bias in our ExtraTrees classifier.

Nevertheless, when you see each curves having a low rating and are related, that’s an indication of excessive bias. In case your curves have a giant hole, that’s an indication of excessive variance.

Listed here are some tips about what to do in each situations:

Excessive Bias:
– Enhance mannequin complexity
– Cut back regularization
– Change mannequin structure
– Add new options

Excessive Variance:
– Add extra samples
– Cut back the variety of options
– Add/enhance regularization
– Lower mannequin complexity
– Mix options
– Change mannequin structure

Characteristic Significance

Similar to in regression fashions, you may inform the magnitude of impression from function coefficients; you are able to do the identical in classification fashions.

In response to your bias-variance prognosis, you might select to drop options or to give you new variables by combining some, in keeping with this graph. Nevertheless, for my mannequin, there isn’t a want to try this. Technically talking, EEG readings is the one function that I’ve, and the extra readings, the higher the classification mannequin will change into.

Hyperparameter Tuning

The following the first step ought to carry out is to tune the knobs in our mannequin, also called hyperparameter tuning. There are a number of methods to do that.

Grid Search

It is a conventional approach for hyperparameter tuning, which means that it was the primary to be developed exterior of manually tuning every hyperparameter. It requires all inputs of related hyperparameters (e.g., all the educational charges you wish to take a look at) and measures the efficiency of the mannequin utilizing cross-validation by going by all attainable mixtures of the hyperparameter values. The downside to this technique is that it will take a very long time to guage when now we have a number of hyperparameters we wish to tune.

Random Search

Random search makes use of random mixtures of the hyperparameter to seek out one of the best performing mannequin. You continue to have to enter all values of the hyperparameters you wish to tune, nonetheless the algorithm searches throughout the grid randomly, as an alternative of looking out all the mixtures of all values of the hyperparameters. This usually beats grid search by way of time because of its random nature the place the mannequin may attain its optimized worth a lot prior to grid search in keeping with this paper.

Genetic Programming

Genetic programming or genetic algorithm (GA) is predicated on Charles Darwin’s concept of survival of the fittest. GA applies small, gradual, and random modifications to the present hyperparameters. It really works by assigning a health worth to an answer, the upper the health worth, the upper the standard of the answer. It then selects the people with the best health values and places them right into a “mating pool” the place two people will generate two offspring (with some modifications utilized to the offspring), which is predicted to have increased high quality than their mother and father. This occurs again and again till we get to the specified optimum worth.

TPOT is an open supply library below energetic growth, first developed by researchers on the College of Pennsylvania. It takes a number of copies of the complete coaching dataset, and performs its personal variation of one-hot encoding (if wanted), then optimizes the hyperparameters utilizing genetic algorithm.

We are going to use dask with tpot’s automl to carry out this. We move xgboost and extratrees classifiers into the tpot config to inform it we solely need the algorithm to carry out searches inside these two classification fashions. We additionally inform tpot to export each mannequin made to a vacation spot in case we wish to cease it early.

Mannequin Analysis

The perfect performing mannequin, with an AUC of 0.997, is the optimized extratrees classifier. Beneath is its efficiency on all three datasets.

We additionally create the ROC curve graph to indicate the above AUC curves.


Now, speaking the important factors of this challenge to a VP or CEO might usually time be the toughest a part of the job, so here’s what I might say to a high-level stakeholder concisely.

On this challenge, we created a classification machine studying mannequin that may predict whether or not sufferers are having a seizure or not by EEG readings. The perfect performing mannequin has a raise metric of 4.3, which means it’s 4.Three occasions higher than simply randomly guessing. It is usually 97.4% appropriate in predicting the constructive courses within the take a look at set. If this mannequin was put into manufacturing to foretell whether or not a affected person is having a seizure, you might anticipate that efficiency in accurately predicting those that are having a seizure.

Thanks for studying!

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