article · Neuroscience Informatics
Biomedical signals such as electrocardiograms and electroencephalograms carry sensitive health data that requires protection during transmission and storage. A systematic review of literature published between 2015 and 2025 analyses 54 studies covering data hiding approaches, specifically steganography and watermarking. The findings categorise technologies into dominant classical approaches and faster-growing advanced methods that incorporate optimisation and learning-based techniques. Electrocardiogram signals receive the vast majority of research attention, followed by electroencephalography, photoplethysmography, and electromyography. Performance evaluation consistently targets imperceptibility, payload capacity, and robustness against distortions. Furthermore, combining data hiding techniques with cryptographic tools and blockchain frameworks appears promising for securing patient health records. Overall, the literature reveals notable gaps in the study of non-electrocardiogram signals and the adoption of intelligent hiding methods.
Digital healthcare systems rely heavily on transmitting sensitive diagnostic recordings across networks. Data hiding methods protect patient confidentiality and verify record authenticity without damaging clinical signal quality. By establishing the current state and blind spots of existing watermarking and steganography methods, this synthesis helps guide the design of more secure and resilient health data architectures.
The reviewed technologies could enable tamper-proof, confidential data sharing for telehealth providers, remote patient monitoring systems, and digital health software developers. Because the underlying research consists of theoretical taxonomies and experimental algorithmic studies, the technology appears to be largely at an early to applied testing stage rather than ready for immediate clinical or commercial deployment without further integration and testing.
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Biomedical signals such as electrocardiography (ECG), electroencephalography (EEG), electromyography (EMG), and photoplethysmography (PPG) contain sensitive clinical information that requires robust protection during storage and transmission. Data hiding techniques, including steganography and watermarking, have been widely investigated to ensure confidentiality, integrity, and authentication while preserving the diagnostic quality of the original signal. This study presents a systematic literature review of biomedical signal data hiding research published between 2015 and 2025. Following the PRISMA methodology, 54 peer-reviewed journal articles were selected for detailed analysis. The reviewed studies were organized into a structured taxonomy comprising Classical and Advanced approaches. Classical methods, which include Time Domain, Transform Domain, and Hybrid Classical techniques, represent the dominant methodological group throughout the observed period. Advanced methods, encompassing Optimization-based, Intelligent, and Hybrid Advanced strategies, demonstrate a higher growth rate, reflecting an emerging shift toward more adaptive and learning-based data hiding solutions. Trend analysis of biomedical signal usage reveals that ECG is the most extensively investigated signal, followed by EEG, PPG, and EMG, suggesting a clear research growth hierarchy across signal modalities. Evaluation practices are primarily centered on imperceptibility, robustness, and capacity, which are most frequently assessed using Percentage Root-mean-square Difference (PRD), Bit Error Rate (BER), and payload-related metrics. Furthermore, security enhancement strategies integrating cryptographic techniques and blockchain-based frameworks are identified as promising directions for strengthening the protection of Patient Health Records (PHR). By consolidating existing taxonomies, trend findings, and evaluation strategies, this review highlights key research gaps, particularly the under exploration of non-ECG signal modalities and the limited adoption of intelligent methods, and provides a structured foundation to guide future developments in biomedical signal data hiding.
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DOI: 10.1016/j.neuri.2026.100292
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