ECG-X: Making ECGs explainable with colour to support early detection of life-threatening heart conditions

在一家繁忙的医院里，一名初级的急诊室医生正在学习如何解读心电。以前，她会计算小方块，试图在打印出来的纸上用三角法手动确定波的开始和结束，而不是在iPad上浏览数据，改变心电的轴线和方向，将颜色应用到曲线下的区域，并放大以了解信号的细节。她用来理解数据的可视化工具还以另一种方式支持她：提供对她正在调查的情况的可能性的自动估计，用自然语言解释它是如何得出判断的。因为自动解释使用了“认知匹配”，即人类和机器共享相同的数据表示形式，所以它在直觉上是可以理解的，并且很容易检查和探索。在这种情况下，通过对心电数据的视觉呈现来提供拟合。支持人类解释的技术，将预先注意处理以突出信号中的异常与临床知识相结合，以确保可靠性和准确性，也被用作机器解释的基础。他没有注意到这会引起任何副作用，但他的智能手表已经提醒他，他的心脏的电活动可能发生了变化，并建议他紧急咨询临床医生。他在紧急会诊时向医生展示了数据，并更换了药物。他的心脏活动很快就会恢复正常。“以上情景表明，我们的研究目标是实现转变--从专家难以手动解释心电图，转变为在家中进行自我监测，以检测可能导致心脏猝死的情况。心电是心脏电活动的图形表示，广泛用于临床实践中检测心脏病理。众所周知，心电解释是复杂的，对人类和机器都构成了挑战。这项研究将开发一套用于询问心电数据的可视化技术，将视觉感知原理与临床知识相结合，以创建决策支持工具，其中人类和机器共享相同的数据表示法。这项研究将有两个方面：第一，可视化技术将与临床医生共同创建，以开发在临床实践中值得信赖的可靠工具，并支撑可用作操作心电数据以辅助解释的基础的理论，并支持自动解释算法。第二，将与公众一起试验和进一步开发特定情况下选定的数据呈现形式，以确定这些技术是否有可能被外行人用于监测自己的心脏健康。我们的长期愿景是设计出临床上可靠和可解释的类人人工智能，使患者或他们的护理人员能够直观地自我监测他们的心电图，以确定临床环境之外的潜在危及生命的心脏疾病。这一愿景最终旨在促进心脏性猝死的一级和二级预防。心脏性猝死是一种灾难性事件，占心血管死亡率的50%，每年在美国造成30万人死亡，在英国造成6万人死亡。导致心源性猝死的心脏电问题通常只有在心电图上才能检测到，缺血性心脏病的早期迹象可以在其他主要症状出现之前在心电图上检测到。因此，改善心电解释对于更早发现潜在的致命心脏疾病至关重要。更快的诊断和自我监测的能力将特别有利于女性，她们经常因为向男性呈现不同的症状而延误治疗。

"In a busy hospital, a junior A&E doctor is learning to interpret ECGs. Where once she would have been counting tiny squares and trying to manually determine the beginning and end of waves using trigonometry - for example drawing tangents - on a paper printout, instead she is exploring the data on an iPad, changing the axes and orientation of the ECG, applying colour to the area under the curve and zooming in to understand details of the signal. The visualisation tool she uses to understand the data also supports her in another way: providing an automated estimation of the likelihood of the conditions she is investigating, explaining in natural language how it has come to its judgement. Because the automated interpretation uses 'cognitive fit' - where the human and the machine share the same representation of the data - it is intuitively understandable, and easy to check and explore. In this case, the fit is provided by the visual presentation of the ECG data. The techniques that support human interpretation, which combine pre-attentive processing to highlight anomalies in the signal with clinical knowledge to ensure reliability and accuracy, are also used as the basis for the machine interpretation.""A patient has started taking a new medication for cancer treatment. He hasn't noticed it causing any side effects, but his smart watch has alerted him that the electrical activity of his heart may have changed, and advises him to consult a clinician urgently. He shows the data to his doctor in an emergency consultation, and has his medication changed. His heart activity soon returns to normal."The scenarios above show the transformation our research aims to achieve - moving from difficult manual interpretation of ECGs by experts, to self-monitoring at home to detect conditions that may lead to sudden cardiac death.The Electrocardiogram (ECG) is a graphical representation of the heart's electrical activity that is widely used in clinical practice for detecting cardiac pathologies. ECG interpretation is known to be complex, challenging both humans and machines. This research will develop a suite of visualisation techniques for interrogating ECG data, combining principles of visual perception with clinical knowledge to create decision support tools where humans and machines share the same representation of the data. The research will have two strands:In the first, visualisation techniques will be co-created with clinicians to develop reliable tools that will be trusted in clinical practice, and underpinning theory that can be used as a foundation for manipulating ECG data to assist interpretation, and support automated interpretation algorithms.In the second, selected forms of data presentation for specific conditions will be trialled and further developed with members of the public, to determine whether the techniques could potentially be used by lay people for monitoring their own cardiac health. Our long-term vision is to engineer clinically reliable and explainable human-like AI that will empower patients or their caregivers to intuitively self-monitor their ECGs for potentially life-threatening cardiac conditions outside the clinical setting. This vision ultimately aims to promote primary and secondary prevention of sudden cardiac death - a catastrophic event accounting for 50% of cardiovascular mortality, causing an estimated 300,000 deaths in the US and 60,000 deaths in the UK annually. Electrical problems with the heart leading to sudden cardiac death are often detectable only on an ECG, and the early signs of ischaemic heart disease can be detected on an ECG before other major symptoms occur. Improving ECG interpretation is thus essential for the earlier detection of potentially lethal heart conditions. Faster diagnosis and the ability to self-monitor will particularly benefit women, who often experience a delay in treatment due to different symptom presentation to men.