Detection of major depressive disorder using vocal acoustic analysis and machine learning—an exploratory study

Abstract

Purpose Diagnosis and treatment in psychiatry are still highly dependent on reports from patients and on clinician judgment. This fact makes them prone to memory and subjectivity biases. As for other medical fields, where objective biomarkers are available, there has been an increasing interest in the development of such tools in psychiatry. To this end, vocal acoustic parameters have been recently studied as possible objective biomarkers, instead of otherwise invasive and costly methods. Patients suffering from different mental disorders, such as major depressive disorder (MDD), may present with alterations of speech. These can be described as uninteresting, monotonous, and spiritless speech and low voice. Methods Thirty-three individuals (11 males) over 18 years old were selected, 22 of which being previously diagnosed with MDD and 11 healthy controls. Their speech was recorded in naturalistic settings, during a routine medical evaluation for psychiatric patients, and in different environments for healthy controls. Voices from third parties were removed. The recordings were submitted to a vocal feature extraction algorithm, and to different machine learning classification techniques. Results The results showed that random tree models with 100 trees provided the greatest classification performances. It achieved mean accuracy of 87.5575% ± 1.9490, mean kappa index, sensitivity, and specificity of 0.7508 ± 0.0319, 0.9149 ± 0.0204, and 0.8354 ± 0.0254, respectively, for the detection of MDD. Conclusion The use of machine learning classifiers with vocal acoustic features appears to be very promising for the detection of major depressive disorder in this exploratory study, but further experiments with a larger sample will be necessary to validate our findings.

Covid-19 diagnosis by combining RT-PCR and pseudo-convolutional machines to characterize virus sequences

“…The most voted class is the departure of Random Forest. Random Forests have been used to solve a plethora of biomedical problems, specially to develop intelligent systems to support diagnosis [48][49][50] . As the most relevant characteristics to determine the decision boundary between classes of virus DNA sequences are unknown, Random Forests can be powerful methods for classification, as they are able to verify many relevant properties through their different trees.…”

“…. , n. Naive Bayesian classifiers are commonly tested against other classifiers in diagnosis support solutions [48][49][50] . Furthermore, they assume all features/predictors have an equal weight.…”

“…In this way, he could identify the characteristics that are particular to each class. Multilayer perceptrons have been used to several biomedical problems, specially to develop intelligent systems to support diagnosis [48][49][50]54 .…”

“…According to the Coverage Theorem, the nonlinear mapping of attribute vectors in representation universes with more dimensions makes it more likely to find linear solutions to discriminate classes. SVMs have been applied to solve several biomedical problems, specially on developing intelligent systems for diagnosis support [48][49][50]54 . Since SVMs are commonly used in biomedical problems, presenting good results, it is reasonable to evaluate a considerable amount of SVM configurations and kernels to solve our problem considering the sequence representation method presented in this work.…”

Comparison of classifiers using robust features for depression detection on Bahasa Malaysia speech

“…Deep learning requires a large training data due to the huge number of parameters needed to be tuned by a learning algorithm and computation power for data processing. Previous studies in the field of ADD using machine learning classifiers have used binary logistic regression (LR) [9], [10], gaussian mixture model (GMM) [11]- [14], discriminant analysis [15], [16], support vector machine (SVM) [11], [17], [18], k-nearest neighbour (KNN) [17], gaussian naïve Bayes (GNB) [19] random forest (RF) [20] and decision tree [21]. The common classification method used for detecting depression in speech are SVM and GMM.…”