Electrocardiogram (ECG) signals are widely used in most remote IoMT systems. Continuous monitoring of patients is required, especially in a pandemic time where doctors recommend telemedicine. This means a massive amount of ECG data is generated, sent to cloud servers, and needs to be shared with legitimate professionals. Therefore, this paper proposes a novel privacy-preserving and efficient technique to reduce the burden on the network while ensuring the privacy of ECG. To ensure efficiency, we use a shallow neural network to learn/remember the ECG shape and represents that in a few neurons. To avoid any loss, the minor residuals between this representation and the original signal is measured and encoded to small footprint using Burrow–Wheeler transform (BWT), followed by move-to-front (MTF) and run-length encoding. To ensure the privacy, only representation neurons are encrypted using a $SessionKey$ obtained from the health authority (HA) server along with $SessionID$ every-time an ECG signal needs to be transmitted. Hence, health authority alone is able to link that $SessionID$ to the patient. Whenever a doctor wants to diagnose an ECG of the patient, HA will share only those two parameters which allow the authorized doctor to see a specific ECG. The model is evaluated using raw ECG data collected from Physio-net. The results obtained are compared and analyzed with the widely used state of art techniques. The results show that the proposed technique outperforms the other techniques by an increase of 50% in size reduction and 60% in transmission time while ensuring the privacy.

心电图（ECG）信号广泛用于大多数远程IoMT系统。需要对患者进行连续监测，尤其是在大流行期间医生建议使用远程医疗。这意味着将生成大量ECG数据，并将其发送到云服务器，并且需要与合法专业人员共享。因此，本文提出了一种新颖的隐私保护和有效的技术，以减轻网络负担，同时确保ECG的隐私。为了确保效率，我们使用浅层神经网络来学习/记住ECG形状，并在少数神经元中进行表示。为了避免任何损失，使用Burrow-Wheeler变换（BWT）测量该表示形式与原始信号之间的次要残差并将其编码为较小的覆盖区，然后进行前移（MTF）和行程编码。为了确保隐私，$\mathrm{小号}Ëss\mathrm{一世}ØñķËÿ$ 从健康授权（HA）服务器以及 $\mathrm{小号}Ëss\mathrm{一世}Øñ\mathrm{一世}d$每次需要发送ECG信号时。因此，仅卫生当局就能将其联系起来$\mathrm{小号}Ëss\mathrm{一世}Øñ\mathrm{一世}d$给病人 每当医生想要诊断患者的ECG时，HA将仅共享这两个参数，以允许授权医生查看特定的ECG。使用从Physio-net收集的原始ECG数据评估模型。使用广泛使用的最新技术对获得的结果进行比较和分析。结果表明，所提出的技术在确保隐私的同时，尺寸减小了50％，传输时间增加了60％，优于其他技术。