Long-term Activity Monitoring Research Articles

Long-term video activity monitoring and anomaly alerting of group-housed pigs

The activity level of group-housed pigs can reflect their daily behavioral patterns and health conditions. Currently, most pig farms mainly rely on manual daily patrols to monitor the activities of group-housed pigs, which is labor-intensive and inaccurate. In this paper, we propose a computer vision-based method for the automatic monitoring and alerting of group-housed pigs activity anomaly. We established a pig segmentation model based on an improved Mask R-CNN incorporating the CBAM attention mechanism and CIoU loss function for segmenting pigs from video frames. The CBAM attention mechanism enables the model to focus more effectively on target regions, while CIoU loss function guides precise learning of object boundaries, outperforming the traditional IoU loss. Grounded on the segmentation results, we invocated a frame difference method to quantify the activity level of group-housed pigs. Subsequently, we adopted the K-means clustering algorithm to explore the daily behaviors of these pigs. Furthermore, we constructed a SARIMA model using historical data to proactively forecast potential shifts in activity levels. The experimental results demonstrated that the accuracy and recall rate of the pig instance segmentation model were 98.7% and 97.9%, respectively. The SARIMA model performed well in predicting activity levels of group-housed pigs with differing health status. For the pig herds under healthy status, the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) were 6.01 and 7.67 respectively, while for the pig herds under unhealthy status, the MAE and RMSE were 6.41 and 9.30 respectively. We proposed an effectively method to monitor daily behavioral patterns and anomalies of group-housed pigs, providing valuable references for pig farm management.

Manufacturing of an electrocardiographic sensor and its implantation technique in rats for long-term monitoring of brain activity

Manufacturing of an electrocardiographic sensor and its implantation technique in rats for long-term monitoring of brain activity

Simultaneous removal of noise and correction of motion warping in neuron calcium imaging using a pipeline structure of self-supervised deep learning models.

Calcium imaging is susceptible to motion distortions and background noises, particularly for monitoring active animals under low-dose laser irradiation, and hence unavoidably hinder the critical analysis of neural functions. Current research efforts tend to focus on either denoising or dewarping and do not provide effective methods for videos distorted by both noises and motion artifacts simultaneously. We found that when a self-supervised denoising model of DeepCAD [Nat. Methods18, 1359 (2021)10.1038/s41592-021-01225-0] is used on the calcium imaging contaminated by noise and motion warping, it can remove the motion artifacts effectively but with regenerated noises. To address this issue, we develop a two-level deep-learning (DL) pipeline to dewarp and denoise the calcium imaging video sequentially. The pipeline consists of two 3D self-supervised DL models that do not require warp-free and high signal-to-noise ratio (SNR) observations for network optimization. Specifically, a high-frequency enhancement block is presented in the denoising network to restore more structure information in the denoising process; a hierarchical perception module and a multi-scale attention module are designed in the dewarping network to tackle distortions of various sizes. Experiments conducted on seven videos from two-photon and confocal imaging systems demonstrate that our two-level DL pipeline can restore high-clarity neuron images distorted by both motion warping and background noises. Compared to typical DeepCAD, our denoising model achieves a significant improvement of approximately 30% in image resolution and up to 28% in signal-to-noise ratio; compared to traditional dewarping and denoising methods, our proposed pipeline network recovers more neurons, enhancing signal fidelity and improving data correlation among frames by 35% and 60% respectively. This work may provide an attractive method for long-term neural activity monitoring in awake animals and also facilitate functional analysis of neural circuits.

Selective recording of physiologically evoked neural activity in a mixed autonomic nerve using a minimally invasive array.

Real-time closed-loop control of neuromodulation devices requires long-term monitoring of neural activity in the peripheral nervous system. Although many signal extraction methods exist, few are both clinically viable and designed for extracting small signals from fragile peripheral visceral nerves. Here, we report that our minimally invasive recording and analysis technology extracts low to negative signal to noise ratio (SNR) neural activity from a visceral nerve with a high degree of specificity for fiber type and class. Complex activity was recorded from the rat pelvic nerve that was physiologically evoked during controlled bladder filling and voiding, in an extensively characterized in vivo model that provided an excellent test bed to validate our technology. Urethane-anesthetized male rats (n = 12) were implanted with a four-electrode planar array and the bladder instrumented for continuous-flow cystometry, which measures urodynamic function by recording bladder pressure changes during constant infusion of saline. We demonstrated that differential bipolar recordings and cross-correlation analyses extracts afferent and efferent activity, and discriminated between subpopulations of fibers based on conduction velocity. Integrated Aδ afferent fiber activity correlated with bladder pressure during voiding (r2: 0.66 ± 0.06) and was not affected by activating nociceptive afferents with intravesical capsaicin (r2: 0.59 ± 0.14, P = 0.54, and n = 3). Collectively, these results demonstrate our minimally invasive recording and analysis technology is selective in extracting mixed neural activity with low/negative SNR. Furthermore, integrated afferent activity reliably correlates with bladder pressure and is a promising first step in developing closed-loop technology for bladder control.

Multisensory System for Long-Term Activity Monitoring to Facilitate Aging-in-Place.

Demographic changes and an ageing population require more effective methods to confront the increased prevalence of chronic diseases which generate dependence in older adults as well as an important rise in social expenditure. The challenge is not only to increase life expectancy, but also to ensure that the older adults can fully enjoy that moment in their lives, living where they wish to (private home, nursing home, …). Physical activity (PA) is a representative parameter of a person's state of health, especially when we are getting older, because it plays an important role in the prevention of diseases, and that is the reason why it is promoted in older adults. One of the goals of this work is to assess the feasibility of objectively measuring the PA levels of older adults wherever they live. In addition, this work proposes long-term monitoring that helps to gather daily activity patterns. We fuse inertial measurements with other technologies (WiFi- and ultrasonic-based location) in order to provide not only PA, but also information about the place where the activities are carried out, including both room-level location and precise positioning (depending on the technology used). With this information, we would be able to generate information about the person's daily routines which can be very useful for the early detection of physical or cognitive impairment.

Inertial measurement data from loose clothing worn on the lower body during everyday activities

Embedding sensors into clothing is promising as a way for people to wear multiple sensors easily, for applications such as long-term activity monitoring. To our knowledge, this is the first published dataset collected from sensors in loose clothing. 6 Inertial Measurement Units (IMUs) were configured as a ‘sensor string’ and attached to casual trousers such that there were three sensors on each leg near the waist, thigh, and ankle/lower-shank. Participants also wore an Actigraph accelerometer on their dominant wrist. The dataset consists of 15 participant-days worth of data collected from 5 healthy adults (age range: 28–48 years, 3 males and 2 females). Each participant wore the clothes with sensors for between 1 and 4 days for 5–8 hours per day. Each day, data were collected while participants completed a fixed circuit of activities (with a video ground truth) as well as during free day-to-day activities (with a diary). This dataset can be used to analyse human movements, transitional movements, and postural changes based on a range of features.

Transient sounds of hydrothermal vents

Transient acoustic signals were recently detected at the Main Endeavour Hydrothermal Vent Field which are believed to be generated by both geological and biological sources, including vent chimney collapse, impulsive geological signals, fish grunts, and snapping. These signals provide an opportunity for long-term passive acoustic monitoring of hydrothermal vent activity and ecology. This method offers advantages of longevity and robustness compared with other monitoring techniques, as the sensor can remain a safe distance away from the high-temperature, caustic vent fluid. Utilizing recordings from a bottom-mounted hydrophone on Ocean Networks Canada’s NEPTUNE observatory, a detector was implemented to identify and classify these signals in more than one year of acoustic data after 2016. While only a single hydrophone is available at this site, an array of three seismic accelerometers also on the NEPTUNE observatory was used to localize transient events when sufficient signal-to-noise ratio was available to provide confidence in the location estimate. Correlation of the transient sounds with seismic activity at the vent field was also evaluated, suggesting that passive acoustic monitoring can augment seismic records to provide additional information regarding geological activity at hydrothermal vent sites.

Usability and ease of use of long-term remote monitoring of physical activity for individuals with acquired brain injury in community: a qualitative analysis.

Objective and continuous monitoring of physical activity over the long-term in the community is perhaps the most important step in the paradigm shift toward evidence-based practice and personalized therapy for successful community integration. With the advancement in technology, physical activity monitors have become the go-to tools for objective and continuous monitoring of everyday physical activity in the community. While these devices are widely used in many patient populations, their use in individuals with acquired brain injury is slowly gaining traction. The first step before using activity monitors in this population is to understand the patient perspective on usability and ease of use of physical activity monitors at different wear locations. However, there are no studies that have looked at the feasibility and patient perspectives on long-term utilization of activity monitors in individuals with acquired brain injury. This pilot study aims to fill this gap and understand patient-reported aspects of the feasibility of using physical activity monitors for long-term use in community-dwelling individuals with acquired brain injury. This pilot study found that patients with acquired brain injury faced challenges specific to their functional limitations and that the activity monitors worn on the waist or wrist may be better suited in this population. The unique wear location-specific challenges faced by individuals with ABI need to be taken into account when selecting wearable activity monitors for long term use in this population.

Characterization of thermal infrared medium- and short-term anomaly information from block to fault in mainland China

The relationship between satellite thermal infrared anomalies and earthquakes or fault activity has been studied for more than 30 years. In this study, five strong earthquakes (the MS6.1 earthquake in Biru, Tibet; the MS7.4 earthquake in Madoi, Qinghai; the MS6.0 earthquake in Delingha, Qinghai; the MS6.1 earthquake in Lushan, Sichuan; and the MS6.8 earthquake in Luding, Sichuan) that occurred on the western mainland of China over the past 2 years were studied. Based on monthly MODIS land surface temperature (LST) data and daily NOAA satellite longwave radiation data, the departure algorithm and the Robust Satellite Techniques (RST) algorithm were used to extract and analyze the characteristics of thermal infrared anomaly information for blocks and faults around the earthquake from different temporal and spatial scales. The results showed the following: 1) In the medium-term scale study based on monthly data, blocks near the epicenters of five earthquakes showed temperature increase anomalies of 1–6 months before the earthquakes; the areas of temperature increase anomalies were clearly controlled by the spatial distribution of the blocks, and earthquakes mostly occurred within blocks with frequent temperature increase anomalies. 2) In the short-term, thermal infrared anomaly feature tracking based on daily data meant that obvious thermal anomalies were also found. The thermal anomalies before the five seismic events all appeared within a period of 3 months before the earthquake, and there were multiple consecutive days of anomalies. The significant temperature increases generally occurred a month before the earthquake, and the distribution of the anomalies was mostly in the form of strips, which is basically consistent with the trend of the fault zone. The use of thermal infrared remote sensing data to summarize the dynamic evolution of thermal infrared anomalies of blocks and faults before strong earthquakes can provide a basis for the long-term monitoring of fault activity and seismic monitoring by satellite thermal infrared technology.

Long-term Engagement in Physical Activity Among Bariatric Surgery Patients: Associations with Treatment Outcomes at 5-Year Follow-up.

Physical activity studies involving bariatric surgery patients tend to be short-term or cross-sectional investigations. Longer-term studies are limited and typically consist of relatively brief objective measurement periods used to generalize activity patterns. Very little research combines objective measurements with structured interviews to determine both the patterns and related factors of long-term physical activity among patients undergoing bariatric surgery. Previous volunteers in a perioperative physical activity study were invited to participate in a mixed methods study investigating physical activity among bariatric patients over a 5-year postoperative period. Fifty-one patients (Mage = 45.4years; 76.5% female, 90.2% White; 86.3% Roux-en-Y procedure) provided interview, survey, accelerometer, and anthropometric data. Participants were divided into four exercise quartiles, based on self-report of their length of regular exercise involvement from 1year before through 5years after surgery. Those reporting the most periods of regular exercise took the most measured steps per day, had greater moderate-to-vigorous physical activity and more bout-related activity, experienced the largest decrease in BMI, and reported the most adherence to nutritional guidelines. Participants reporting the most and least physical activity found the measurement periods to be most similar to their normal activity patterns. While physical activity increased significantly after surgery, measured physical activity did not reach recommended levels for steps or exercise bout minutes. Measured physical activity and self-reported physical activity show congruent trends among patients undergoing bariatric surgery. More research is needed to determine optimal long-term monitoring and promotion of physical activity among patients.

Continuous Long-Term Physical Activity Monitoring in Hemodialysis Patients.

Physical inactivity is common in patients receiving hemodialysis, but activity patterns throughout the day and in relation to dialysis are largely unknown. This knowledge gap can be addressed by long-term continuous activity monitoring, but this has not been attempted and may not be acceptable to patients receiving dialysis. Ambulatory patients with end-stage kidney disease receiving thrice-weekly hemodialysis wore commercially available wrist-worn activity monitors for 6 months. Step counts were collected every 15 minutes and were linked to dialysis treatments. Physical function was assessed using the Short Physical Performance Battery (SPPB). Fast time to recovery from dialysis was defined as ≤2 hours. Mixed effects models were created to estimate step counts over time. Of 52 patients enrolled, 48 were included in the final cohort. The mean age was 60 years, and 75% were Black or Hispanic. Comorbidity burden was high, 38% were transported to and from dialysis by paratransit, and 79% had SPPB <10. Median accelerometer use (199 days) and adherence (95%) were high. Forty-two patients (of 43 responders) reported wearing the accelerometer every day, and few barriers to adherence were noted. Step counts were lower on dialysis days (3991 [95% CI, 3187 to 4796] versus 4561 [95% CI, 3757 to 5365]), but step-count intensity was significantly higher during the hour immediately after dialysis than during the corresponding time on nondialysis days (188 steps per hour increase [95% CI, 171 to 205]); these levels were the highest noted at any time. Postdialysis increases were more pronounced among patients with fast recovery time (225 [95% CI, 203 to 248] versus 134 [95% CI, 107 to 161] steps per hour) or those with SPPB ≥7. Estimates were unchanged after adjustment for demographics, diabetes status, and ultrafiltration rate. Long-term continuous monitoring of physical activity is feasible in patients receiving hemodialysis. Highly granular data collection and analysis yielded new insights into patterns of activity after dialysis treatments.

Design, Fabrication and Performance Assessment of Flexible, Microneedle-Based Electrodes For ECG Signal Monitoring.

Microneedle-based electrodes have attracted significant attention for the monitoring of physiological signals, including ECG, EMG, and E OG, as they have the potential to eliminate the skin preparation and stability issues associated with conventional wet gel electrodes. This paper describes the development of a polymeric flexible microneedle electrode (FMNE) that does not require skin abrasion and can be used for long-term ECG monitoring. Fabricated using a combination of epoxy resin microneedles bonded to a flexible substrate, the performance of the FMNE was compared to that of a conventional wet-gel electrode by simultaneously capturing the ECG signal using both electrodes, and estimating the signal-to-noise ratio (SNR) of each. Results show that the flexible electrode can acquire ECG signals in which all the characteristic components of the wave are visible, and that are comparable in quality to those obtained using commercial wet electrodes. Bland-Altman plots were drawn to validate the performance of FMNE, and show that the mean difference ± standard deviation in SNR obtained using wet electrodes and FMNE was [Formula: see text]. Clinical Relevance- These microneedle-based 'dry electrodes' could be used in long-term monitoring of biopotential activity.

A Comparison of Heart Pulsations Provided by Forcecardiography and Double Integration of Seismocardiogram.

Seismocardiography (SCG) is largely regarded as the state-of-the-art technique for continuous, long-term monitoring of cardiac mechanical activity in wearable applications. SCG signals are acquired via small, lightweight accelerometers fixed on the chest. They provide timings of important cardiac events, such as heart valves openings and closures, thus allowing the estimation of cardiac time intervals of clinical relevance. Forcecardiography (FCG) is a novel technique that records the cardiac-induced vibrations of the chest wall by means of specific force sensors, which proved capable of monitoring respiration, heart sounds and infrasonic cardiac vibrations, simultaneously from a single contact point on the chest. A specific infrasonic component captures the heart walls displacements and looks very similar to the Apexcardiogram. This low-frequency component is not visible in SCG recordings, nor it can be extracted by simple filtering. In this study, a feasible way to extract this information from SCG signals is presented. The proposed approach is based on double integration of SCG. Numerical double integration is usually very prone to large errors, therefore a specific numerical procedure was devised. This procedure yields a new displacement signal (DSCG) that features a low-frequency component (LF-DSCG) very similar to that of the FCG (LF-FCG). Experimental tests were carried out using an FCG sensor and an off-the-shelf accelerometer firmly attached to each other and placed onto the precordial region. Simultaneous recordings were acquired from both sensors, together with an electrocardiogram lead (used as a reference). Quantitative morphological comparison confirmed the high similarity between LF-FCG and LF-DSCG (normalized cross-correlation index >0.9). Statistical analyses suggested that LF-DSCG, although achieving a fair sensitivity in heartbeat detection (about 90%), has not a very high consistency within the cardiac cycle, leading to inaccuracies in inter-beat intervals estimation. Future experiments with high-performance accelerometers and improved processing methods are envisioned to investigate the potential enhancement of the accuracy and reliability of the proposed method.

Non-Contact Electrocardiograms Acquisition Method Based on Capacitive Coupling

Electrocardiogram (ECG) monitoring is an essential method for medical diagnosis [1], [2]. With the development of technology, better quality physiological signals can be easily recorded [3]. Long-term monitoring of electrocardiographic activity is helpful to monitor potential cardiac dysfunction. The Ag/Agcl electrode with conductive gel can record ECG signals with good signal quality and has been widely used in hospitals. However, the gel needs to be replaced regularly to achieve good signal quality because the quality of the signal deteriorates due to the gel drying over a long time. In addition, wet electrodes may not be suitable for long-term ECG monitoring because they may cause skin redness, swelling, and discomfort, especially for those patients with allergic reactions. Also, direct contact between electrodes and the human body is not applicable in many cases, such as skin injury patients, infants, etc. The goals of disease monitoring technology based on physiological signals are to reduce the amount invasive of equipment, increase the comfort of patients and improve the quality of life while pursuing high-quality signals.

Simultaneous Wireless Power and Data Transfer: Methods to Design Robust Medical Implants for Gastrointestinal Tract

Implantable medical systems for long-term monitoring of bioelectrical activity of the gastrointestinal (GI) tract, known as slow waves, and for treating GI dysmotility and functional disorders through electrical stimulation pulses have emerged over the recent years. These implants construct a bidirectional interface between the clinician and the patient&#x0027;s GI tract, to record and monitor slow waves and to provide electrical therapies. Because of the limited battery life of the implants, wireless power transfer (WPT) is a fundamental requirement to conduct studies and provide treatments for an extended period. Furthermore, the WPT link provides the opportunity to establish near-field data communication over the same link. Currently, there are three main wireless power and data transfer (WPDT) approaches for GI tract implants consisting of resonant inductive, ultrasonic, and capacitive couplings. Each approach provides a tradeoff based on the size of the implant, depth of implantation <i>in vivo</i>, the amount of power received by the implant, and the WPT efficiency. In this review, we present the theory and an overview of the major research works accomplished for each of the aforementioned WPT approaches. In particular, this paper focuses on simultaneous WPDT for systems implanted in the GI tract, while fluctuations of the wireless link efficiency and reliability due to body movements and stomach motility are investigated, and techniques are introduced to improve the WPDT fidelity.

Putting Temperature into the Equation: Development and Validation of Algorithms to Distinguish Non-Wearing from Inactivity and Sleep in Wearable Sensors.

Long-term monitoring of real-life physical activity (PA) using wearable devices is increasingly used in clinical and epidemiological studies. The quality of the recorded data is an important issue, as unreliable data may negatively affect the outcome measures. A potential source of bias in PA assessment is the non-wearing of a device during the expected monitoring period. Identification of non-wear time is usually performed as a pre-processing step using data recorded by the accelerometer, which is the most common sensor used for PA analysis algorithms. The main issue is the correct differentiation between non-wear time, sleep time, and sedentary wake time, especially in frail older adults or patient groups. Based on the current state of the art, the objectives of this study were to (1) develop robust non-wearing detection algorithms based on data recorded with a wearable device that integrates acceleration and temperature sensors; (2) validate the algorithms using real-world data recorded according to an appropriate measurement protocol. A comparative evaluation of the implemented algorithms indicated better performances (99%, 97%, 99%, and 98% for sensitivity, specificity, accuracy, and negative predictive value, respectively) for an event-based detection algorithm, where the temperature sensor signal was appropriately processed to identify the timing of device removal/non-wear.

1D U-NET++: AN EFFECTIVE METHOD FOR BALLISTOCARDIOGRAM J-PEAK DETECTION

Heart rate measurement through Ballistocardiogram (BCG) signal is an efficient method for long-term cardiac activity monitoring in real-time, especially for patients with cardiovascular and cerebrovascular disease. In this study, we propose a one-dimensional (1D) U-net++ to identify the position of J-peak in BCG signals automatically. The proposed 1D U-net++ is based on a 1D convolution neural network through dense skip connection backward transfer data features. The low-level and high-level data features of the BCG signals are combined with the last layer features of 1D U-net++ to shorten the semantic gap when the encoder and decoder feature skip connection. The BCG signals of eight healthy subjects were collected for experimental verification, and the accuracy and precision of J-peak detection reached 99.4% and 99.3%, respectively. The experimental results demonstrate that our proposed method can effectively identify J-peak in BCG signal.

Validity of the Aktibipo Self-rating Questionnaire for the Digital Self-assessment of Mood and Relapse Detection in Patients With Bipolar Disorder: Instrument Validation Study.

BackgroundSelf-reported mood is a valuable clinical data source regarding disease state and course in patients with mood disorders. However, validated, quick, and scalable digital self-report measures that can also detect relapse are still not available for clinical care.ObjectiveIn this study, we aim to validate the newly developed ASERT (Aktibipo Self-rating) questionnaire—a 10-item, mobile app–based, self-report mood questionnaire consisting of 4 depression, 4 mania, and 2 nonspecific symptom items, each with 5 possible answers. The validation data set is a subset of the ongoing observational longitudinal AKTIBIPO400 study for the long-term monitoring of mood and activity (via actigraphy) in patients with bipolar disorder (BD). Patients with confirmed BD are included and monitored with weekly ASERT questionnaires and monthly clinical scales (Montgomery-Åsberg Depression Rating Scale [MADRS] and Young Mania Rating Scale [YMRS]).MethodsThe content validity of the ASERT questionnaire was assessed using principal component analysis, and the Cronbach α was used to assess the internal consistency of each factor. The convergent validity of the depressive or manic items of the ASERT questionnaire with the MADRS and YMRS, respectively, was assessed using a linear mixed-effects model and linear correlation analyses. In addition, we investigated the capability of the ASERT questionnaire to distinguish relapse (YMRS≥15 and MADRS≥15) from a nonrelapse (interepisode) state (YMRS<15 and MADRS<15) using a logistic mixed-effects model.ResultsA total of 99 patients with BD were included in this study (follow-up: mean 754 days, SD 266) and completed an average of 78.1% (SD 18.3%) of the requested ASERT assessments (completion time for the 10 ASERT questions: median 24.0 seconds) across all patients in this study. The ASERT depression items were highly associated with MADRS total scores (P<.001; bootstrap). Similarly, ASERT mania items were highly associated with YMRS total scores (P<.001; bootstrap). Furthermore, the logistic mixed-effects regression model for scale-based relapse detection showed high detection accuracy in a repeated holdout validation for both depression (accuracy=85%; sensitivity=69.9%; specificity=88.4%; area under the receiver operating characteristic curve=0.880) and mania (accuracy=87.5%; sensitivity=64.9%; specificity=89.9%; area under the receiver operating characteristic curve=0.844).ConclusionsThe ASERT questionnaire is a quick and acceptable mood monitoring tool that is administered via a smartphone app. The questionnaire has a good capability to detect the worsening of clinical symptoms in a long-term monitoring scenario.

A novel 3D-printed multi-driven system for large-scale neurophysiological recordings in multiple brain regions

BackgroundElectrical probes have been widely used for recording single-unit spike activity and local field potentials (LFPs) in brain regions. However, setting up an easily-assembled large-scale recording in multiple brain regions for long-term and stable neural activity monitoring is still a hard task. New methodWe established a novel 3D-printed multi-drive system with high-density (up to 256 channels) tetrodes/grid electrodes that enables us to record cortical and subcortical brain regions in freely behaving animals. ResultsIn this paper, we described the design and fabrication of this system in detail. By using this system, we obtained successful recording on both spikes and LFPs from seven distinct brain regions that are related to memory function. Comparision with existing methodsThe low cost, large-scale electrodes with small size and flexible 3D-printed design of the system allow us to implant assembled tetrodes or grid electrodes into multiple target brain areas. ConclusionsThe 3D-printed large-scale multi-drive platform we described here may serve as a powerful new tool for future studies of brain circuitry functions.

Neurophysiological phenotypes of pharmacoresistant temporal lobe epilepsy

Patients with a drug-resistant form of epilepsy can be treated by neurosurgery through the destruction or separation of the epileptic focus. If the results of clinical, neuro-imaging and neurophysiological methods are discordant, then the localization of the epileptogenic zone is performed based on the results of long-term invasive monitoring of the bioelectrical activity of the cortex and deep structures of the brain. The aim of this work was the retrospective analysis of the results of invasive monitoring of the bioelectrical activity of the brain to clarify the mechanisms of the formation of patterns of interictal and ictal activity in structural epilepsy. The study included 35 patients (18 men, 17 women) with drug-resistant temporal lobe epilepsy, who were treated at the Polenov Neurosurgical Institute. The examination included video-EEG monitoring, long-term invasive monitoring of bioelectrical activity of the cortex, and deep brain structures. The patients were divided into two groups according to the type of surgical treatment: 1) micro-surgical resection of the epileptic focus, including the zone of structural changes (24 patients); 2) stereotactic destruction of the amygdala-hippocampal complex (6 patients). The follow-up of the outcomes of the surgical treatment took place over 2-3 years. Depending on the results of the surgical treatment, the patients were divided into two groups: 1) patients with a favorable outcome (Engel 1–2) — 15 patients and 2) patients with no positive dynamics and a relatively poor outcome (Engel 3–4) — 15 patients. The results obtained showed that the patterns of interictal and ictal activity in their totality determine the neurophysiology, i.e the phenotype of temporal lobe epilepsy, reflecting the interference of pathogenetic and sanogenetic mechanisms. The localization of the epileptogenic zone should be based on the cumulative assessment of interictal and ictal activity. The presence of more than one focus of interictal activity, the secondary spread of epileptiform activity from the primary focus, are prognostically unfavorable factors.