Does a PCT Read ECG? Can AI read ECG strips?
- viatomtechnology
- 1 day ago
- 8 min read
Quick answer: Patient care technicians can perform preliminary ECG assessments. They may identify vast QRS complexes or ST deviations. When it comes to interpreting ECG strips precisely, it would be wise to involve cardiology professionals. However, things are looking grim—the number of cardiologists with the expertise and also willingness to read ECGs is decreasing. In the United States, most ECGs are interpreted by non-cardiologists (including emergency physicians, internists, and family physicians) who gain less clinical ECG training relatively.[1]
To break the limitations, healthcare institutions introduce AI-driven platforms, releasing physicians’ pressure and improving their diagnosis efficiency and patient throughput in the emergency room. Yet, they may miss context-specific medication interactions and subtle ECG evolution. That's where physician oversight helps integrate clinical touches and provide accurate final ECG strip interpretations.
Challenges in ECG Reading
Skill Limitations
A study on ICU nurses found that 63.9% agreed they did not have adequate knowledge to interpret patients' ECG readings, and 45.4% reported not being well-trained for ECG interpretation.[2]
Nurses and patient care technicians may spot gross oddities but may not be confident enough to proceed beyond this recognition. Many practitioners never receive structured advanced training after their initial nursing or medical school exposure. This becomes a matter to solve, especially when we see the result of 69.6% of doctors telling the truth about being nervous about interpreting ECG in the acute setting.[3]
They may flag a wide QRS or a missing P wave when reading an ECG strip. Yet, they lack the official clearance to define treatment pathways. A cross-sectional study (including 323 staff and students of Ardabil University of Medical Sciences in northwestern Iran) showed that 77.3% of health professional staff and students were unable to identify normal sinus rhythm, and 63.8% of them were unable to identify acute myocardial infarction.[4]
Even well-structured in-service programs do not fully address the gap between routine identification and the deeper minutiae of arrhythmia management. The frontline providers must use physician oversight for clinical decisions.
Interpretation Complexity
Reading ECG strips requires analyzing minute waveform deviations that can look similar across pathologies. A meta-analysis of 78 studies found that the accuracy of ECG interpretation was generally low, even after training. Physicians at all levels, including cardiologists, showed performance gaps, with a pooled accuracy of 74.9% among cardiologists.[5]
Ventricular rhythms can mimic supraventricular variants if the baseline noise is high. ST-segment changes may overlap with benign early repolarization. Even senior staff of cardiology fellows and expert hospitalists can disagree on the impact of mild ST depression. Expert consensus is key since clinical context and serial tracings can alter the conclusion.
Data Quality Issues
Artifacts from patient movement, electrode misplacement, or electrical interference may warp critical sections of the waveform. Frequent retesting might be necessary when reading ECG strips in high-acuity settings, including ICUs, busy emergency departments, and ambulances. Motion artifacts result in sudden irregularities in the ECG baseline. This irregular signal may lead PCTs to identify it as premature contractions or interfere with ECG wave shapes or other supraventricular and ventricular arrhythmias.[6]
Some digital ECGs can be partially unreadable because of baseline wander or lead noise. Filter algorithms may decrease such distortions but mask subtle conduction delays or micro-volt T-wave changes.
Overview of Mainstream ECG Reading Algorithms
While the challenges in ECG reading pose significant clinical burdens, they have also driven the development of advanced computational tools. In response to these limitations, a range of algorithmic approaches have emerged to support or automate ECG analysis.
Uni-G ECG Algorithm[7]
The University of Glasgow's Uni-G ECG analysis program has been under constant development for over two decades. It processes 8 or 11 leads recorded at 500 samples per second. Initially, it applies a notch filter to exclude AC interference. Next, it detects noise and fiducial points like the onset of the P wave. Notably, it accommodates neonates and adults and considers racial variations in wave amplitudes. It offers serial ECG comparison using two approaches.
Philips DXL ECG Algorithm[8] & QT Interval Measurement Algorithms[9]
Philips' DXL algorithm interprets resting ECGs while analyzing up to 18 leads. For QT interval measurement, it employs an alpha-trimming technique with a measure of central tendency to conclude the median QT value from the eight most reliable leads. A lead's reliability is assessed per the variance of beat-to-beat onset and offset determinations. It excludes leads with small amplitudes, high respiratory variation, or significant noise for accurate QT measurements.
GE Marquette 12SL ECG Analysis Programs[10]
GE Healthcare's Marquette 12SL ECG analysis program has been refined since its introduction in 1980. It delivers precise measurements and interpretations for rapid diagnosis. The program spots acute ischemic syndromes and supports serial ECG comparisons for validated analyses. Also, it offers reproducible and accurate QT measurements and interpretations. The program also detects bi-ventricular pacemakers and underlying rhythms for insights during ECG analysis.
HES® Hannover ECG System[11]
The Hannover ECG System (HES®) delineation process comprises four main steps. These are preprocessing, feature extraction, classification, and post-processing. Initially, preprocessing addresses noise reduction and baseline wander correction. After that, feature extraction identifies P, QRS complexes, and T waves. Classification assigns such features to specific cardiac events. Last but not least, post-processing refines the analysis while considering contextual information. Such a structured approach guarantees a reliable interpretation of ECG strips for accurate diagnoses.
Applications of ECG Reading Algorithm in Different Scenarios and Cases
Hospital Diagnostic Equipment: Uni-G ECG Algorithm & GE Marquette 12SL ECG
In hospital settings, the Uni-G ECG Algorithm can be integrated into diagnostic equipment. Uni-G procedures have evolved over a long period of time, adapting to medical changes and the introduction of new terminology. The Uni-G ECG interpretation program is based on simultaneous recordings in 8 or 11 leads with a sampling rate of 500 times per second. A 50 Hz or 60 Hz trap filter is first applied to remove AC interference.
It performs a complex selection procedure to determine the type of averaged ECG using QRS detection and waveform classification. It offers measurements across populations, including neonates and adults. Yet, its complexity may demand training for reading ECG strips. The algorithm helps diagnose myocardial infarction and arrhythmias. For example, its application in high-resolution ECG machines heightens detection accuracy.​[12]
The Marquette 12SL is also widely used in hospitals for its advanced ECG analysis capabilities, including gender-specific criteria for acute myocardial infarction (MI) detection and QT interval measurement. It provides detailed diagnostic statements and supports serial comparisons, enhancing diagnostic confidence and efficiency.[10]
Emergency and Pre-Hospital Settings:Â Philips DXL ECG Algorithm
The Philips DXL ECG Algorithm might be used in emergency scenarios. Its rapid analysis of up to 18 leads facilitates quick decision-making. Still, in high-motion environments, artifacts can affect reading ECG strips. This algorithm provides advanced STEMI detection, helping promptly identify the probable site of coronary occlusion, and supports rapid decision-making for reperfusion therapy. Portable defibrillators with the DXL algorithm are an example of its utility in pre-hospital care. [13]
There is another case that shows its application in re-analyzing raw digital ECG signals. This step aims to identify if automated interpretation statements on the pre-hospital ECGs are sensitive enough to diagnose acute MI and acute ischemia.[14]
Long-Term Holter Monitoring: Philips QT Interval Measurement Algorithm
The Philips real-time QT interval measurement algorithm is used in hospital settings for continuous monitoring of patients, particularly those at risk of QT prolongation due to medication. This algorithm combines features from both the Philips DXL 12-lead and ambulatory Holter QT algorithms, providing real-time QT alarms and trending capabilities. [15]
Remote and Telemedicine Applications: HES® Hannover ECG System
The HES® Hannover ECG System can be embedded in DSPs or microcontrollers, allowing it to be integrated into portable devices suitable for remote monitoring. A study developed a platform-independent web-based telecardiology system that uses the HES algorithm for ECG interpretation. This system allows for the recording and uploading of ECG data using a wireless ECG recorder, facilitating remote ECG analysis and interpretation.[16]
Clinical Research and Big Data Analysis: Philips DXL ECG Algorithm & GE Marquette 12SL ECG Analysis Programs
The Philips DXL ECG Algorithm is capable of analyzing up to 18 leads of ECG data. Plus, it can be integrated into systems that support high-resolution ECG analysis. These features make it suitable for big data analysis. It is validated against large datasets and used in clinical trials to ensure its accuracy and reliability. For example, the ICE study aims to validate the DXL algorithm's performance in the HeartStart Intrepid Monitor/Defibrillator.[17]
The Marquette 12SL program is validated against global, clinically verified databases, ensuring its reliability in clinical research settings. This validation supports its use in analyzing large datasets for studies on disease patterns and treatment outcomes. The program's serial comparison feature is valuable in research studies for tracking changes in ECG waveforms over time, aiding in the analysis of disease progression or response to treatment.[18]
Viatom's AI-ECG Platform
​The varied applications illustrate the growing impact of AI-powered ECG interpretation in both routine and high-stakes clinical scenarios. Building on these industry-wide advancements, the AI-ECG platform significantly delivers accurate, comprehensive, and high-speed diagnostic performance in real-world clinical environments.
 AI-ECG Platform employs convolutional neural networks to analyze ECG waveforms for a sensitivity above 99% across 12 arrhythmic events. It permits rapid interpretation of ECG data while identifying over 104 types of abnormal events within 16 categories. For instance, a 5-minute ECG recording might be analyzed in just one second. Our platform has processed more than 14 million ECG services across over 7,100 institutions. Such an efficiency decreases physicians' time reading ECG strips for improved diagnostic workflows. ​
Wrapping Up
In short, while patient care technicians can assist in primary ECG reading, accurate interpretation still rests on the combined strengths of clinical expertise and emerging technology.
AI-powered ECG platforms bridge the gap between growing diagnostic demands and limited specialist availability—transforming raw waveform data into actionable insights within seconds. These platforms rely on both robust algorithms and reliable hardware integration to ensure high-quality signal capture and real-time analysis.
As AI continues to evolve, its role in ECG interpretation will expand—not to replace physicians, but to augment them. The future of ECG reading lies in intelligent collaboration: devices that sense, algorithms that analyze, and clinicians who contextualize.
Reference:
[1] Finding ECG Readers in Clinical Practice. Available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC4466111/ (Accessed May 7, 2025)
[2] ICU nurses’ knowledge and attitude towards electrocardiogram interpretation in Fujian province, China: a cross-sectional study. Available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC10568621/ (Accessed March 17, 2025)
[3]Â Fear of electrocardiogram interpretation (ECGphobia) among medical students and junior doctors. Available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC9875878/ (Accessed March 24, 2025)
[4]Â Assessment of electrocardiogram interpretation competency among healthcare professionals and students of Ardabil University of Medical Sciences: a multidisciplinary study - PubMed. Available at: https://pubmed.ncbi.nlm.nih.gov/35681191/ (Accessed March 17, 2025)
[5] Accuracy of Physicians’ Electrocardiogram Interpretations - A Systematic Review and Meta-analysis. Available at: https://jamanetwork.com/journals/jamainternalmedicine/fullarticle/2771093 (Accessed March 17, 2025)
[6]Â Main artifacts in electrocardiography. Available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC6931710/ (Accessed March 20, 2025)
[7]Â The University of Glasgow (Uni-G) ECG analysis program. Available at: https://www.researchgate.net/publication/4219614_The_University_of_Glasgow_Uni-G_ECG_analysis_program (Accessed March 28, 2025)
[8] Philips DXL Algorithm for resting ECGs. Available at: https://www.usa.philips.com/healthcare/product/HCNOCTN68/philips-dxl-algorithm-resting-ecgs (Accessed March 28, 2025)
[9]Â Philips QT Interval Measurement Algorithms for Diagnostic, Ambulatory, and Patient Monitoring ECG Applications. Available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC6932579/ (Accessed March 28, 2025)
[10] Marquette 12SL ECG Analysis Programs. Available at: https://www.gehealthcare.com/products/marquette-12sl?srsltid=AfmBOorFDnR38pjxWlg3tzpZczmXx-xi73sfo5PYx2E49uNhl-iQXmXF (Accessed April 02, 2025)
[11] Analyzing the Delineation Precision of Hannover ECG System (HES): A Validation Study. Available at: https://www.cinc.org/archives/2011/pdf/0617.pdf (Accessed April 02, 2025)
[12]Â The University of Glasgow (Uni-G) ECG Analysis Program. Available at:
https://cinc.org/archives/2005/pdf/0451.pdf (Accessed April 02, 2025)
[13] Philips DXL ECG Algorithm for the HeartStart MRx - Application Note. Available at: https://www.documents.philips.com/doclib/enc/fetch/577817/577869/DXL_ECG_Algorithm__for_the_HeartStart_MRx_-_Application_Note_(ENG).pdf (Accessed April 07, 2025)
[14] Performance and Limitations of Automated ECG Interpretation Statements in Patients with Suspected Acute Coronary Syndrome. Available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC8664995/ (Accessed April 07, 2025)
[15] Philips QT Interval Measurement Algorithms for Diagnostic, Ambulatory, and Patient Monitoring ECG Applications. Available at: https://pmc.ncbi.nlm.nih.gov/articles/PMC6932579/ (Accessed April 07, 2025)
[16] Development of Platform-Independent Web-Based Telecardiology Application for Pilot Case Study. Available at: https://eprints.utm.my/59224/1/KamKuiLin2015_DevelopmentofPlatformIndependentWeb.pdf (Accessed April 08, 2025)
[17] Philips HeartStart Intrepid 12-lead ECG Study – The ICE study. Available at: https://cdn.clinicaltrials.gov/large-docs/32/NCT05636332/Prot_SAP_000.pdf (Accessed April 08, 2025)
[18] A Collaborative Platform for Advancing Automatic Interpretation in ECG Signals. Available at: https://www.mdpi.com/article/10.3390/diagnostics14060600?type=check_update&version=1 (Accessed April 08, 2025)