**4. Study for the optimization of the ECT**

In this section, characteristics of the electroconvulsive therapy (ECT) domain are reported to identify why it is appropriate the usage of the KDSM methodology to manage the knowledge in this particular domain and to subsequently use it in the optimization of the ECT, with special emphasis on the reaction time parameter and others strongly related to it.

### **4.1 Background**

An interesting field of psychiatric study concerns therapies for various severe and treatment-resistant psychiatric disorders, such as major depression or schizophrenia. ECT is used worldwide as a safe, effective, rapid, valuable, and widely used treatment for patients with major depression, bipolar disorder, psychosis. ECT is a biological treatment procedure that involves the brief application of an electrical stimulus to produce a generalized seizure appropriate to the therapeutic response

and improve the psychiatric condition, which, for example, in the case of intractable catatonia and neuroleptic malignant syndrome, ECT could be life-saving. For patients who do not tolerate or respond poorly to medications and who are at high risk of drug-induced toxicity or toxic drug–drug interactions, ECT is the safest treatment option [15, 16]. Even in certain conditions associated with neuropsychiatric disorders, such as parkinsonism, dementia, and stroke, ECT is effective. Currently, the aim is to optimize the use of ECT and, in correlation with other biological and psychological treatments, to reduce the impact of side effects, prevent relapses and the recurrence of symptoms. Although several of the brain biological events related to its efficacy are still unknown, the physiological response to ECT has been studied through heart rate, blood pressure, electrocardiogram effects, cardiac enzymes, electroencephalogram effects, or hormonal response among others. However, for the time being, there is no formalized technique applied to this therapy and one interesting avenue is the study of the neuropsychological effects of ECT on psychophysiological parameters such as reaction, decision, or motor times. These effects are cognitive changes related to orientation, attention and calculation, memory loss and recall [17], and the aim is to make progress in identifying the factors that cause them, a direct influence in the cognitive state of the patient, by analyzing the effect on reaction times related to visual and audible stimuli after ECT application.

Some studies in similar situations in psychiatry, neurology, or various areas of medicine have reviewed everything from simple comparisons of treatments to longer-term effects or pre-post responses to several treatments or medications. Increasingly, there is a need to detect more subtle or domain-specific effects that add complicating factors. In these situations, simplifying serial measures to summary statistics can facilitate analysis by removing the time element. This approach of using summary statistics does facilitate clear communication of the main results, both in simple terms for the public and with full reporting of individual responses to the scientific community. However, the graphical presentation of serial measures in a line graph does not allow for easy plotting of individual or paired responses. Measures of central tendency can illustrate group effects in graphs and figures, but individual responses to each experimental condition are presented and lost from view, especially when sample sizes are minimal and do not facilitate the critical evaluation of the data [18].

### **4.2 Data description**

For this case study, information was available for 183 patients with very severe depressive disorder or schizophrenia, according to both ICD-10 and DSM-5 criteria, for whom ECT was indicated and who gave informed consent to undergo ECT and participate in the study. Information is available on patients' main characteristics, conditions, somatic status, routine hematological and biochemical tests, chest Xray, electrocardiograms, history of alcohol or other drug abuse or dependence, anesthetic risk, comorbidities, age, weight, education … It is worth mentioning that the dataset belongs to the Health Research Unit https://www.uis.com.mx/index.php which is a private organization. Therefore, the characteristics and attributes of the dataset are detailed only in small extracts in order not to make inappropriate use of the support provided by the aforementioned organization.

Standard practice optimizes the therapeutic relationship, in the selection of electrical stimulus parameters such as energy level, stimulus duration, pulse width, and pulse rate. In addition, multiple patient responses are monitored by electroencephalogram, electrocardiogram, and electromyogram, including a rigorous assessment of the patient's neuropsychological effects. For the measurement of

## *Toward Optimization of Medical Therapies with a Little Help from Knowledge Management DOI: http://dx.doi.org/10.5772/intechopen.101987*

psychophysiological parameters, the Vienna Test System (VTS) was used at 2, 4, 6, 12, and 24 h after each electroshock (ES) application. The VTS provides various stimuli and records the ability to react to them, recording reaction time (RT) for single-choice and compound-choice stimuli. Different modes of light and sound stimuli are available, with a choice of red, yellow, or white, so that different combinations of stimuli can be created simultaneously or sequentially for reaction time measurement. The VTS offers 8 test forms:

**S1:** Simple reaction, yellow—reaction to critical stimulus.

**S2:** Simple reaction, tone—reaction to critical stimulus.

**S3:** Choice reaction, yellow/tone—reaction to critical stimulus combination.

**S4:** Choice reaction, yellow/red—reaction to critical stimulus combination.

**S5:** Choice reaction, yellow/tone, yellow/red—reaction to critical stimulus combination.

**S6:** Simple reaction, white under monotonous conditions.

**S7:** Measurement of alertness—simple reaction, yellow (with acoustic cue).

**S8:** Measurement of alertness—simple reaction, tone (with optical cue).

The use of a rest key and a reaction key makes it possible to distinguish between reaction time and motor time. The main areas of use are those in which reaction times are measured, such as traffic psychology, personnel psychology (safety assessments), sports psychology, and psychopharmacology. In recent years, the use of reaction time measurements has also increased in neurology, psychiatry, rehabilitation, and occupational medicine. The way to interact with the VTS is for study participants to react, as quickly as possible, to visual or acoustic stimuli. The reaction is recorded after pressing or releasing a button when presented with a stimulus, which can be a yellow or red light, an audible tone, or a combination of these stimuli. The following measures were recorded for this particular study: erroneous decisions, erroneous reactions, absence of reactions, incorrect reactions, correct reactions, decision times, driving times, and reaction times [15, 19] and, these measurements were made for four specific tests: simple visual (e5: S1), simple auditory (e6: S2), complex visual (e7: S4), and complex visual–auditory (e8: S5).

### **4.3 Description of the situation presented in the ECT ISD**

In this domain of ECT, a representation of a set of patients as (*i*<sup>1</sup> … *in*) in which a *ni* occurrences of a given *ES* take place at different times (*E*<sup>1</sup> … *En*). Connected to the occurrence of each electroshock, there are psychophysiological parameters denoted by *Y* (for this particular case the reaction time, RT) that reflect the performance behavior of the patient. Therefore, a few *Y* measurements are taken for each patient and each *ES* occurrence. For this particular case, *r* is a very small fixed number of times, moments in time, that *Y* will be measured each time just after an *ES* is applied. The measurement record is made during the first 24 h after application, in particular at 2 h, 4 h, 6 h, 8 h, 12 h, and 24 h after an *ES*. The recording and monitoring of RT are of particular interest for the study of side effects resulting from ECT. Such a scenario generates three types of information that can be organized in 3 matrices:


The number of *ES* and the timing of their application may differ from patient to patient without any underlying pattern. However, all applications of *ES* to the same patient are influenced by patient characteristics, which means that all *RT* measurements on the same patient are influenced by patient characteristics. Therefore, in matrices *Y* and *Z*, each patient acts as a blocking factor, establishing, in the matrix *Y*, bundles of records of measurements of the attribute of interest, very short and repeated serial measurements, which follow the application of each *ES* on the same patient and which are not independent of each other. Consequently, the characteristics of matrices *X* (patient characteristics *X*<sup>1</sup> … *XK*), *Y* (serial measurements of the parameter of interest *RT*), and *Z* (ECT characteristics *Z*<sup>1</sup> … *ZL*) differ and the knowledge that interrelates them is non-trivial and complies with the previous description of the ISD (section §3).

Furthermore, it is very easy to appreciate the inconvenience of simplifying *RT* measurements to a single serial measurement for each patient, as shown in **Figures 5** and **6**, the mean *RT* curve (black line), since potentially relevant information is lost, as the variability depends on both ES and patient characteristics and the efficacy of ECT may be affected. This action would modify the structure of the matrices to allow for a classical analysis, although such an action is not recommended.

Moreover, the loss of potentially relevant information is easily observed, as the variability is linked to both ES and patient characteristics and, consequently, the efficacy of ECT is affected. Even the variability is significant between curves, making it difficult to find a general pattern. However, for representing knowledge and communicating it, it is possible to do so using the mean curve and visualize a very general trend of the evolution of the patient's response to ECT. And as there is no fixed number of ES applications applied to a patient, the effect it exerts must be considered. Considering the above, and due to the lack of *a priori knowledge* for the training of machine learning tools, knowledge management must be performed by KDSM.

### **4.4 Knowledge management by KDSM**

KDSM is a methodology for Knowledge Management and Knowledge Discovery in Informally Structured Domains where very short and repeated serial measurements with a blocking factor are presented (see **Figure 7**). Following good knowledge management practice, KDSM is developed in three phases:

### **Figure 5.**

*VIC graphic of RT curves of the e8 test of the 1st patient. It shows the evolution of the RT through its curves for a 6 ES ECT applied to the 1st patient. The RT correspond to the complex visual-audible (e8) and was measured at 2, 4, 6, 12, and 24 hours after applying each ES.*

*Toward Optimization of Medical Therapies with a Little Help from Knowledge Management DOI: http://dx.doi.org/10.5772/intechopen.101987*

### **Figure 6.**

*VIC graphic of RT curves of the e8 test of the 4th patient. It shows the evolution of the RT through its curves for an ECT of 5 ES applied to the 4th patient. The RT correspond to the complex visual-audible (e8) and was measured at 2, 4, 6, 12, and 24 hours after applying each ES.*

### **Figure 7.**

*Three phases of KDSM methodology for knowledge discovery in informally structured domains where very short and repeated serial measurements with a blocking factor are presented.*


In short, the methodology will carry out the next tasks:

**BLA:** Individuals Baselines Analysis

