Recording Device Research Articles

Language Teaching Optimization: Enhancing the Distinction Between /i:/ and // for Second Language Learners

One of the biggest challenges encountered by second language learners is to accurately differentiate between the two vowels in English: /i:/ and //, which play an important role in everyday communication and are crucial for clear transmission of information and effective communication due to their high functional load (FL). Therefore, the paper aims to gain a more profound understanding of the difficulties learners encounter in distinguishing these vowels, with the hope of providing them with more effective learning strategies and methods. In this paper, seven English majors and seven non-English majors were selected as the study participants using an empirical research method, and audio data on the participants' pronunciation of words containing /i:/ and // were collected using a recording device and analyzed using Praat. The results indicates that both groups frequently lacked an understanding of the distinctive pronunciation characteristics of these vowels, which resulted in instances of pronunciation confusion or inaccuracy. Moreover, it proposes suggestions for enhancing teaching strategies: the integration of audio and visual teaching resources and the implementation of targeted pronunciation exercises can facilitate the acquisition of English pronunciation, thereby enhancing the accuracy and fluency of their spoken English and their performance in English communication.

The martensitic structures, exchange bias effect and training effect in Ni-Mn-Ga ribbons

The exchange bias effect is the physical basics of spin valves and magnetic recording devices. A stable exchange bias effect would be suitable for practical application. In this work, Ni50Mn50-xGax (11≤ x ≤16) ribbons were obtained by melt spinning. The grain size and microstructure have been tuned through melt spinning, which changes the exchange interaction in ribbons and thus gets a giant and stable exchange bias effect. As the increase of x, the structure of Ni50Mn50-xGax (11≤ x ≤16) ribbons undergoes a transition from non-modulated martensite to non-modulated martensite &10 layers modulated martensite to 10 layers modulated martensite. The average grain size of all ribbons is less than 10 μm, and the martensitic plate widths are smaller than 200 nm. With the increasing x or the cooling fields, the magnetization of the ribbons has been enhanced. As a result, the competition between ferromagnetic and antiferromagnetic interactions has been changed and thus influencing spin glass behavior. Furthermore, a low degree of attenuation for the exchange bias effect after continuous cycles has been achieved which arises from the stable spin configuration with a larger contribution of frozen spin which has a slow relaxation rate. For example, the exchange bias field of 8.78 kOe in Ni50Mn38Ga12 ribbons can be achieved under the 40 kOe field cooling at 10 K, and the exchange bias field after infinite cycles can be kept at 8.58 kOe, the degree of attenuation is only 3.4 %. This work enriches the research on the exchange bias effect of Heusler alloys and provides the experimental basis and theoretical support for designing a stable exchange bias effect.

Bioelectronic Implantable Devices for Physiological Signal Recording and Closed‐Loop Neuromodulation

AbstractBioelectronic implantable devices are adept at facilitating continuous monitoring of health and enabling the early detection of diseases, offering insights into the physiological conditions of various bodily organs. Furthermore, these advanced systems have therapeutic capabilities in neuromodulation, demonstrating their efficacy in addressing diverse medical conditions through the precise delivery of stimuli directly to specific targets. This comprehensive review explores developments and applications of bioelectronic devices within the biomedical field. Special emphasis is placed on the evolution of closed‐loop systems, which stand out for their dynamic treatment adjustments based on real‐time physiological feedback. The integration of Artificial Intelligence (AI) and edge computing technologies is discussed, which significantly bolster the diagnostic and therapeutic functions of these devices. By addressing elemental analyses, current challenges, and future directions in implantable devices, the review aims to guide the pathway for advances in bioelectronic devices.

Derivative Method to Detect Sleep and Awake States through Heart Rate Variability Analysis Using Machine Learning Algorithms.

Sleep disorders can have harmful consequences in both the short and long term. They can lead to attention deficits, as well as cardiac, neurological and behavioral repercussions. One of the most widely used methods for assessing sleep disorders is polysomnography (PSG). A major challenge associated with this method is all the cables needed to connect the recording devices, making the examination more intrusive and usually requiring a clinical environment. This can have potential consequences on the test results and their accuracy. One simple way to assess the state of the central nervous system (CNS), a well-known indicator of sleep disorder, could be the use of a portable medical device. With this in mind, we implemented a simple model using both the RR interval (RRI) and its second derivative to accurately predict the awake and napping states of a subject using a feature classification model. For training and validation, we used a database providing measurements from nine healthy young adults (six men and three women), in which heart rate variability (HRV) associated with light-on, light-off, sleep onset and sleep offset events. Results show that using a 30 min RRI time series window suffices for this lightweight model to accurately predict whether the patient was awake or napping.

Computer vision-guided open-source active commutator for neural imaging in freely behaving animals.

Recently developed miniaturized neural recording devices that can monitor and perturb neural activity in freely behaving animals have significantly expanded our knowledge of neural underpinning of complex behaviors. Most miniaturized neural interfaces require a wired connection for external power and data acquisition systems. The wires are required to be commutated through a slip ring to accommodate for twisting of the wire or tether and alleviate torsional stresses. The increased trend toward long-term continuous neural recordings has spurred efforts to realize active commutators that can sense the torsional stress and actively rotate the slip ring to alleviate torsional stresses. Current solutions however require the addition of sensing modules. Here, we report on an active translating commutator that uses computer vision (CV) algorithms on behavioral imaging videos captured during the experiment to track the animal's position and heading direction in real time and uses this information to control the translation and rotation of a slip ring commutator to accommodate for accumulated mouse heading orientation changes and position. The CV-guided active commutator has been extensively tested in three separate behavioral contexts. We show reliable cortex-wide imaging in a mouse in an open field with a miniaturized wide-field cortical imaging device. Active commutation resulted in no changes to measured neurophysiological signals. The active commutator is fully open source, can be assembled using readily available off-the-shelf components, and is compatible with a wide variety of miniaturized neurophotonic and neurophysiology devices.

A simplified model for predicting ventricular hypertrophy than traditional methods

Ventricular hypertrophy is the thickening of the muscles in the ventricular chambers of the heart which stiffens and thickens the cardiac walls giving rise to many complications like arrhythmia, high blood pressure, or even heart failure. One of the ways to diagnose ventricular hypertrophy is through an Echocardiogram which can be predicted by the positive large amplitude of Chest Leads [V4–V6] of an Electrocardiogram (ECG). But chest lead application is one of the complicated more parts of the ECG than limb lead. Here we have tried to predict ventricular hypertrophy with bipolar limb lead electrocardiogram [L-I, L-II & L-III] without any chest leads which can be easily designed to make an ECG prototype instead of any sophisticated instruments to increase its global accessibility even in remote areas for the prediction of ventricular hypertrophy even may be for other diagnosis in future. We acquired electrocardiogram data sets from the physio-net database, specifically collected data from both normal and hypertrophic patients in an institute. These data sets were obtained using the same recording device. The Lead-I of the RVH and normal patients were not satisfactorily distinguishable. However, in this work, it has been established that Lead-I can be used to predict for identification of LVH patients.

Evaluation of pediatric patients with palpitations via cardiac event recorder, Holter monitoring and transesophageal electrophysiologic study in detecting arrhythmia

Objective: Palpitations are a common reason for referral to pediatric cardiologists, the diagnostic workup involves ambulatory, non-invasive recording devices and invasive procedures. We aimed to evaluate arrhythmic symptoms of pediatric patients with a cardiac event recorder, Holter monitoring and transesophageal electrophysiologic study (TEEPS) results. Materials and Methods: Retrospective evaluation of pediatric patients who fitted an event recorder at tertiary University Hospital between January 2002 and August 2012. The data obtained from the same patients’ as cardiac event recorder, Holter monitoring and TEEPS results were studied for comparison. Results: During the study period, 40 patients who had all data of cardiac event recorder, Holter monitoring and TEEPS were included. The median age of the patients included in the study was 12 [interquartile range, 7-15] years. Using the Holter monitoring, supraventricular extrasystoles (SVEs) were detected in six (15%) patients and ventricular extrasystoles (VESs) in three (7.5%). According to the event recorder data of the 40 patients, there was sinus tachycardia in 20 (50%), supraventricular tachycardia (SVT) in three (7.5%), and SVEs in two (5%) patients. The event recorder data of the remaining 15 patients were assessed as normal. The analysis of the TEEPS results revealed atrioventricular nodal reentrant tachycardia in four (10%), atrioventricular reentry tachycardia in three (7.5%), and normal results in the remaining 33 (82.5%) patients. Conclusions: This study demonstrated that a cardiac event recorder may also be considered as an important diagnostic tool in the diagnosis and ruling out of SVT in pediatric patients for whom the cause of arrhythmia cannot be identified with the Holter monitor, in patients who do not accept the use of TEEPS method following the use of Holter monitor, and in patients in whom SVT cannot be stimulated by TEEPS yet complaints persist.

Development of fault location functions in a real digital fault recorder: Computational strategies and validation

This paper shares experiences acquired during the development of a fault location module (FLM) in a real Digital Fault Recorder and Phasor Measurement (DFRPM) device, which has been designed for the Itaipu utility, Brazil. Hardware and software aspects, as well as computational strategies and innovative solutions used to implement the FLM are addressed. The DFRPM fault location module (DFRPM-FLM) is validated by means of simulations in a Real-Time Digital Simulator, considering fault scenarios in high- and low-inertia systems. The case studies reveal that the adopted computational strategies resulted in a flexible and reliable DFRPM-FLM, which showed to be able to automatically estimate the fault location, without the need for human intervention.

A Video App for Screening Osgood-Schlatter Disease Using Soccer Instep Kicking Motion Analysis.

Osgood-Schlatter disease (OSD) is a type of osteochondrosis and traction apophysitis that results from repeated contractions of the quadriceps femoris muscle on the tibial tuberosity. Its prevention, early diagnosis, and treatment are crucial because it causes chronic knee pain and surgical approaches are required if left untreated. Three-dimensional motion analysis is a useful approach for elucidating the pathological factors of OSD; however, it requires advanced cameras, sophisticated facilities, and expensive software. Conversely, the advent of technology has provided affordable video recording devices, and smartphone/tablet-based applications have enabled two-dimensional (2D) motion analysis. This emerging tool and artificial intelligence technology were used to analyze the pivot leg from videos recorded on a tablet device during the instep kicks of adolescent soccer players. Therefore, in this study, we aimed to determine whether the pathological factors for OSD occurring in the pivot foot can be identified through a simple 2D motion analysis using a tablet device. In total, 94 knees of 47 soccer players (aged 14.1±0.8 years, all male) who belong to a single soccer club were evaluated. OSD was diagnosed using ultrasonography and physical examination (a positive bone fragment on ultrasonography or tenderness at the tibial tuberosity). Lower limb muscle tightness was evaluated using the finger-floor distance, straight leg raising test, heel-buttock distance, Thomas test, and ankle range of motion using a goniometer. We then performed motion analysis, and the instep kicking motion was recorded using a video camera on a tablet device. The joint angles of the hip, knee, and ankle were measured using a real-time human-pose detection system. Data were compared between the OSD and non-OSD groups. Overall, six of the 47 players (12.8%) were diagnosed with OSD. No correlation was found between lower limb tightness and the occurrence of OSD in all indices. However, the 2D motion analysis revealed that the knee flexion angle at the time of plantar placement during the instep kick movement was significantly larger in the OSD group than in the non-OSD group (OSD group: 42.0±7.2˚, non-OSD group: 33.5±6.6˚, *p=0.013). A video motion analysis revealed that the knee flexion angle during the instep kicking motion was significantly greater in athletes with OSD of the supporting foot.

A versatile recording device for the analysis of continuous daily external activity in colonies of highly eusocial bees.

As pollinators, bees are key to maintaining the biodiversity of angiosperm plants, and for agriculture they provide a billion-dollar ecosystem service. But they also compete for resources (primarily nectar and pollen), especially the highly social bees that live in perennial colonies. So, how do they organize their daily temporal activities? Here, we present a versatile, low-cost device for the continuous, automatic recording and data analysis of the locomotor activity in the colony-entrance tube of highly eusocial bees. Consisting of an in-house built block containing an infrared detector, the passage of bees in the colony entrance tunnel is registered and automatically recorded in an Arduino environment, together with concomitant recordings of temperature and relative humidity. With a focus on the highly diverse Neotropical stingless bees (Meliponini), we obtained 10-day consecutive recordings for two colonies each of the species Melipona quadrifasciata and Frieseomelitta varia, and also for the honey bee. The Lomb-Scargle periodogram analysis identified a predominant circadian rhythmicity for all three species, but also indications of ultradian rhythms. For M. quadrifasciata, which is comparable in size to the honey bee, we found evidence for a possibly anticipatory activity already before sunrise. As all three species also presented activity at night in the colony entrance tube, this also raises questions about sleep organization in social insects. The cost and versatility of the device and the open-source options for data analysis make this an attractive system for conducting studies on circadian rhythms in social bees under natural conditions, complementing studies on flower visits by these important pollinators.

Acoustic estimation of the manatee population and classification of call categories using artificial intelligence

The population sizes of manatees in many regions remain largely unknown, primarily due to the challenging nature of conducting visual counts in turbid and inaccessible aquatic environments. Passive acoustic monitoring has shown promise for monitoring manatees in the wild. In this study, we present an innovative approach that leverages a convolutional neural network (CNN) for the detection, isolation and classification of manatee vocalizations from long-term audio recordings. To improve the effectiveness of manatee call detection and classification, the CNN works in two phases. First, a long-term audio recording is divided into smaller windows of 0.5 seconds and a binary decision is made as to whether or not it contains a manatee call. Subsequently, these vocalizations are classified into distinct vocal classes (4 categories), allowing for the separation and analysis of signature calls (squeaks). Signature calls are further subjected to clustering techniques to distinguish the recorded individuals and estimate the population size. The CNN was trained and validated using audio recordings from three different zoological facilities with varying numbers of manatees. Three different clustering methods (community detection with two different classifiers and HDBSCAN) were tested for their suitability. The results demonstrate the ability of the CNN to accurately detect manatee vocalizations and effectively classify the different call categories. In addition, our study demonstrates the feasibility of reliable population size estimation using HDBSCAN as clustering method. The integration of CNN and clustering methods offers a promising way to assess manatee populations in visually challenging and inaccessible regions using autonomous acoustic recording devices. In addition, the ability to differentiate between call categories will allow for ongoing monitoring of important information such as stress, arousal, and calf presence, which will aid in the conservation and management of manatees in critical habitats.

Quantifying and mitigating recorder-induced variability in ecological acoustic indices

Due to the complexity of soundscapes, Ecological Acoustic Indices (EAI) are frequently used as metrics to summarize ecologically meaningful information from audio recordings. Recent technological advances have allowed the rapid development of many audio recording devices with significant hardware/firmware variations among brands, whose effects in calculating EAI have not yet been determined. In this work, we show how recordings of the same landscape with different devices effectively hinder reproducibility and produce contradictory results. To address these issues, we propose a preprocessing pipeline to reduce EAI variability resulting from different hardware without altering the target information in the audio. To this end, we tested eight EAI commonly used in soundscape analyses. We targeted three common cases of variability caused by recorder characteristics: sampling frequency, microphone gain variation, and frequency response. We quantified the difference in the probability density functions of each index among recorders according to the Kullback-Leibler divergence. As a result, our approach reduced up to 75% variations among recorders from different brands (AudioMoth and SongMeter) and identified the conditions in which these devices are comparable. In conclusion, we demonstrated that different devices effectively affect EAI and show how these variations can be mitigated.

Diabetes mellitus and hard braking events in older adult drivers

BackgroundDiabetes mellitus (DM) can impair driving safety due to hypoglycemia, hyperglycemia, diabetic peripheral neuropathy, and diabetic eye diseases. However, few studies have examined the association between DM and driving safety in older adults based on naturalistic driving data.MethodsData for this study came from a multisite naturalistic driving study of drivers aged 65–79 years at baseline. Driving data for the study participants were recorded by in-vehicle recording devices for up to 44 months. We used multivariable negative binomial modeling to estimate adjusted incidence rate ratios (aIRRs) and 95% confidence intervals (CIs) of hard braking events (HBEs, defined as maneuvers with deceleration rates ≥ 0.4 g) associated with DM.ResultsOf the 2856 study participants eligible for this analysis, 482 (16.9%) reported having DM at baseline, including 354 (12.4%) insulin non-users and 128 (4.5%) insulin users. The incidence rates of HBEs per 1000 miles were 1.13 for drivers without DM, 1.15 for drivers with DM not using insulin, and 1.77 for drivers with DM using insulin. Compared to drivers without DM, the risk of HBEs was 48% higher for drivers with DM using insulin (aIRR 1.48; 95% CI: 1.43, 1.53).ConclusionOlder adult drivers with DM using insulin appear to be at increased proneness to vehicular crashes. Driving safety should be taken into consideration in DM care and management.

Modelling reindeer rut activity using on-animal acoustic recorders and machine learning.

For decades, researchers have employed sound to study the biology of wildlife, with the aim to better understand their ecology and behaviour. By utilizing on-animal recorders to capture audio from freely moving animals, scientists can decipher the vocalizations and glean insights into their behaviour and ecosystem dynamics through advanced signal processing. However, the laborious task of sorting through extensive audio recordings has been a major bottleneck. To expedite this process, researchers have turned to machine learning techniques, specifically neural networks, to streamline the analysis of data. Nevertheless, much of the existing research has focused predominantly on stationary recording devices, overlooking the potential benefits of employing on-animal recorders in conjunction with machine learning. To showcase the synergy of on-animal recorders and machine learning, we conducted a study at the Kutuharju research station in Kaamanen, Finland, where the vocalizations of rutting reindeer were recorded during their mating season. By attaching recorders to seven male reindeer during the rutting periods of 2019 and 2020, we trained convolutional neural networks to distinguish reindeer grunts with a 95% accuracy rate. This high level of accuracy allowed us to examine the reindeers' grunting behaviour, revealing patterns indicating that older, heavier males vocalized more compared to their younger, lighter counterparts. The success of this study underscores the potential of on-animal acoustic recorders coupled with machine learning techniques as powerful tools for wildlife research, hinting at their broader applications with further advancement and optimization.

Electrolyte engineering in organic electrochemical transistors for advanced electrophysiology

Electrophysiology is an indispensable tool in the early diagnosis of a wide range of diseases, making the precise, continuous, and stable recording of electrophysiological signals critically important. Organic electrochemical transistors stand out among various electrophysiological recording devices, offering a high signal-to-noise ratio due to their intrinsic amplification capability. However, despite their inherent advantages, several challenges persist in practical scenarios, such as the stability of wearable devices, limited spatiotemporal resolution, and undesired inter-channel crosstalk in implantable systems. Addressing these challenges may require innovative approaches in electrolyte engineering. This perspective summarizes the latest advancements and ongoing hurdles in the electrolyte engineering of organic electrochemical transistors, highlighting their potential to revolutionize advanced electrophysiological applications.

Where's Whaledo: A software toolkit for array localization of animal vocalizations.

Where's Whaledo is a software toolkit that uses a combination of automated processes and user interfaces to greatly accelerate the process of reconstructing animal tracks from arrays of passive acoustic recording devices. Passive acoustic localization is a non-invasive yet powerful way to contribute to species conservation. By tracking animals through their acoustic signals, important information on diving patterns, movement behavior, habitat use, and feeding dynamics can be obtained. This method is useful for helping to understand habitat use, observe behavioral responses to noise, and develop potential mitigation strategies. Animal tracking using passive acoustic localization requires an acoustic array to detect signals of interest, associate detections on various receivers, and estimate the most likely source location by using the time difference of arrival (TDOA) of sounds on multiple receivers. Where's Whaledo combines data from two small-aperture volumetric arrays and a variable number of individual receivers. In a case study conducted in the Tanner Basin off Southern California, we demonstrate the effectiveness of Where's Whaledo in localizing groups of Ziphius cavirostris. We reconstruct the tracks of six individual animals vocalizing concurrently and identify Ziphius cavirostris tracks despite being obscured by a large pod of vocalizing dolphins.

Biomechanics Analysis of Normal Jump Shot and Fade-Away Jump Shot in Basketball

Basketball players normally use jump shots to score points during a game. The player must shoot the ball depending on their own experience that requires a long period of time to train. The player can learn faster and master the shooting skills with the assistance of video and data analysis. This paper determines the scoring effectiveness, the body posture and release angle of normal and fade–away jump shots in basketball using the biomechanics analysis. Recording devices were used to record the jump shots and the results were analysed by using several software including Wondershare Filmora, KMPlayer, Openpose, MATLAB, and Logger Pro. With the aid of biomechanics formula, MATLAB and Logger Pro software, the shooting angle, acceleration, energy, force, etc data were obtained. Results showed that the average angle of release that scored for both normal and fade-away jump shots were 59.4 and 59.5 degrees respectively. Normal jump shots had been concluded as more efficient due to 15 % higher scoring than the fade-away jump shots while they required 9.5 % less power to perform. Therefore, it was found that the improvement to the Openpose software can benefit many people. In potential future application, instead of detecting human posture, detecting objects might be a breakthrough for future application around the world.

Leveraging AI and Machine Learning to Develop and Evaluate a Contextualized User-Friendly Cough Audio Classifier for Detecting Respiratory Diseases: Protocol for a Diagnostic Study in Rural Tanzania.

Respiratory diseases, including active tuberculosis (TB), asthma, and chronic obstructive pulmonary disease (COPD), constitute substantial global health challenges, necessitating timely and accurate diagnosis for effective treatment and management. This research seeks to develop and evaluate a noninvasive user-friendly artificial intelligence (AI)-powered cough audio classifier for detecting these respiratory conditions in rural Tanzania. This is a nonexperimental cross-sectional research with the primary objective of collection and analysis of cough sounds from patients with active TB, asthma, and COPD in outpatient clinics to generate and evaluate a noninvasive cough audio classifier. Specialized cough sound recording devices, designed to be nonintrusive and user-friendly, will facilitate the collection of diverse cough sound samples from patients attending outpatient clinics in 20 health care facilities in the Shinyanga region. The collected cough sound data will undergo rigorous analysis, using advanced AI signal processing and machine learning techniques. By comparing acoustic features and patterns associated with TB, asthma, and COPD, a robust algorithm capable of automated disease discrimination will be generated facilitating the development of a smartphone-based cough sound classifier. The classifier will be evaluated against the calculated reference standards including clinical assessments, sputum smear, GeneXpert, chest x-ray, culture and sensitivity, spirometry and peak expiratory flow, and sensitivity and predictive values. This research represents a vital step toward enhancing the diagnostic capabilities available in outpatient clinics, with the potential to revolutionize the field of respiratory disease diagnosis. Findings from the 4 phases of the study will be presented as descriptions supported by relevant images, tables, and figures. The anticipated outcome of this research is the creation of a reliable, noninvasive diagnostic cough classifier that empowers health care professionals and patients themselves to identify and differentiate these respiratory diseases based on cough sound patterns. Cough sound classifiers use advanced technology for early detection and management of respiratory conditions, offering a less invasive and more efficient alternative to traditional diagnostics. This technology promises to ease public health burdens, improve patient outcomes, and enhance health care access in under-resourced areas, potentially transforming respiratory disease management globally. PRR1-10.2196/54388.

Facile synthesis of co-axially electrospun Co-C nanofibers and their ferromagnetic behavior

We describe a simple co-axial electrospinning approach followed by a carbonisation process to create cobalt-carbon (Co-C) nanofibers that are then thoroughly analysed using various techniques. X-ray diffraction measurements showed the creation of pure crystalline cobalt with face-centered cubic (fcc) structure, and average crystallite size was determined using the Debye–Scherrer formula. The average crystallite size has been calculated to be in the range of 10 − 15 nm. According to the Raman investigation, all Co-C nanofibers have an amorphous carbon structure with little graphitic behaviour. Field emission scanning electron microscopy was used to determine the shape and average diameter of electrospun nanofibers. The field-dependent magnetic characterisation demonstrated a satisfactory ferromagnetic behaviour with maximum saturation magnetisation values of 10, 10.2, and 11.2 emu/g for Co12.5-C sample at 300, 100, and 5 K, respectively. Compared to bulk cobalt, the produced Co-C nanofibers have a high coercivity value. With average crystallite size, the coercivity varies. Again, magnetisation versus temperature measurements have supported the existence of ferromagnetism because there is no evidence of blocking temperature or any transitional behaviour below 300 K. As a result, applications for microwave absorption, catalysis, and several magnetic recording devices can benefit from the coupling of ferromagnetic properties with carbon nanofiber materials.

Electrocardiographic approach strategies in patients with Parkinson disease treated with deep brain stimulation.

Deep brain stimulation (DBS) is an interdisciplinary and reversible therapy that uses high-frequency electrical stimulation to correct aberrant neural pathways in motor and cognitive neurological disorders. However, the high frequency of the waves used in DBS can interfere with electrical recording devices (e.g., electrocardiogram, electroencephalogram, cardiac monitor), creating artifacts that hinder their interpretation. The compatibility of DBS with these devices varies and depends on factors such as the underlying disease and the configuration of the neurostimulator. In emergencies where obtaining an electrocardiogram is crucial, the need for more consensus on reducing electrical artifacts in patients with DBS becomes a significant challenge. Various strategies have been proposed to attenuate the artifact generated by DBS, such as changing the DBS configuration from monopolar to bipolar, temporarily deactivating DBS during electrocardiographic recording, applying frequency filters both lower and higher than those used by DBS, and using non-standard leads. However, the inexperience of medical personnel, variability in DBS models, or the lack of a controller at the time of approach limit the application of these strategies. Current evidence on their reproducibility and efficacy is limited. Due to the growing elderly population and the rising utilization of DBS, it is imperative to create electrocardiographic methods that are easily accessible and reproducible for general physicians and emergency services.